Mobile sensor-based community gaming for improving vocational students’ sleep and academic outcomes

Computers & Education 151 (2020) 103812

Contents lists available at ScienceDirect

Computers & Education journal homepage: http://www.elsevier.com/locate/compedu

Mobile sensor-based community gaming for improving vocational students’ sleep and academic outcomes Jeen-Shing Wang a, Jeffrey Hugh Gamble b, Ya-Ting Carolyn Yang c, * a b c

Department of Electrical Engineering, National Cheng Kung University No.1, University Road, Tainan City, 701, Taiwan Department of Foreign Languages, National Chiayi University 85, Wenlong Village, Minhsiung County, Chiayi, 62103, Taiwan Institute of Education & Center for Teacher Education, National Cheng Kung University No.1, University Road, Tainan City, 701, Taiwan

A R T I C L E I N F O

A B S T R A C T

Keywords: Cooperative/collaborative learning Distributed learning environments Learning communities Secondary education Teaching/learning strategies

Quality sleep is critical for teenagers’ physical and mental health and, consequently, learning achievement. Technology, particularly the use of mobile sensors and digital game-based learning, has the potential to enhance students’ sleep hygiene, reducing insomnia and daytime sleepiness and improving students’ academic performance. Therefore, this study implemented and evalu­ ated a sleep hygiene instruction intervention in terms of three elements: a) mobile sleep sensor data feedback for sleep self-evaluation; b) a collaborative-competitive mobile community game (MCG) for sleep promotion based on social-interdependence; and c) an instructional intervention adopting a social cognitive approach. To validate the efficacy of the instructional design, a pretest-posttest quasi-experiment was conducted with 114 10th grade students from three classes of an urban vocational high school in Taiwan. The three intact classes were randomly assigned to one of three sleep hygiene courses: a comparison group (37 students receiving sleep sensor feedback), experimental group one (E1; 38 students receiving sleep sensor feedback and adopting MCG), and experimental group two (E2; 39 students receiving sleep sensor feedback, adopting MCG, and taking a social cognitive-based course). The empirical results suggest that the use of sleep sensor feedback and the MCG (E1 and E2) effectively improved the sleep behaviors of vocational students. In fact, inclusion of the mobile sensor with feedback on sleep quality was sufficient to provide improvement in both sleep and academic outcomes for all students. These results demonstrate the promising potential of mobile community-based technological in­ terventions for improving sleep hygiene, relieving insomnia daytime sleepiness, when integrated with either traditional or social cognitive-based sleep courses. Specific implications and recom­ mendations for the development of technology-enhanced sleep-related or health promotion courses are provided.

1. Introduction 1.1. Research motivation and contribution Sleep impacts every facet of life, including neural development (Whalley, 2014), linguistic development (James, Gaskell, Weighall, * Corresponding author. E-mail addresses: [email protected] (J.-S. Wang), [email protected], [email protected] (J.H. Gamble), [email protected] (Y.-T.C. Yang). https://doi.org/10.1016/j.compedu.2020.103812 Received 5 July 2019; Received in revised form 2 January 2020; Accepted 12 January 2020 Available online 13 January 2020 0360-1315/© 2020 Published by Elsevier Ltd.

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

& Henderson, 2017), cognition and behavior (Crowley, Wolfson, Tarokh, & Carskadon, 2018), stress management (Sanford, Yang, Wellman, Liu, & Tang, 2010), and emotion (Brand et al., 2016; Goldstein & Walker, 2014; Palmer & Alfano, 2017). Sleep is acknowledged as a critical factor in learning and behavior, with adolescents demonstrating an increased biological need for sleep, despite an overall observed reduction in sleep duration beginning in secondary school (Mitru, Millrood, & Mateika, 2002). In fact, adolescents are particularly vulnerable to sleep issues since four main areas of sleep regulation occur during pubertal development, including decreases in the length and depth of sleep, a shift towards adult REM sleep patterns, increased daytime sleepiness, and a tendency towards evening-type sleep patterns, particularly in terms of later bedtimes and wake times (Dahl & Carskadon, 1995). However, in the current Taiwanese educational system, nearly all students must get up around 6 or 7 o’clock to arrive at school before 7:30 in the morning. Thus, the sleep quality of Taiwanese adolescents is a major concern, with research showing 27% of ad­ olescents have difficulty falling asleep; 32% waking up in the middle of the night, 22% waking up too early, 47% feeling tired due to poor sleep, and 28% reporting a bad mood due to poor sleep (Song & Gao, 2000). As a result, 87% of in Beebe et al. (2008) teenagers reported sleeping fewer hours than required, experiencing significant difficulties in emotional management, impulse control, responsiveness, and attention. This issue is not exclusive to Taiwan, since, according to recent statistics, as research has shown that 68% of American high school students slept 7 h or less on school nights, with a shocking 17% sleeping 5 h or less on school nights (Wheaton, Olsen, Miller, & Croft, 2016). This is, on average, less than the recommended sleep duration of eight to 10 h, as proposed by the National Sleep Foundation (Hirshkowitz et al., 2015). Taiwan, while adopting a compulsory 12 year national education policy, does provide students choices in terms of secondary education. Some students may choose to attend regular (academic-oriented) high schools, with an emphasis on preparation for tertiary education, while others may choose vocational, or “skills-based” education to complete their high school diploma. Vocational pro­ grams, while offering a wider range of courses based on future career choices, also emphasizes the practice of skills (such as intern­ ships). Vocational programs may also be an attractive alternative for students who have lower academic performance and motivation due to underlying lifestyle, home, or financial factors. Research has shown that vocational students are more likely to engage in parttime work, take practical-based supplementary courses in the evening, while balancing academic, social, and leisure considerations (Lo & Wu, 2007). Potentially due to the influence of these external factors, research has shown that technical and vocational education (TVE) students have irregular daily schedules that impact physiological and psychological well-being (Huang, Wang, & Guilleminault, 2010). While high school students who attend non-vocational schools also face study and test pressures, the burnout of vocational students, in particular, has emphasized in the literature (Gerber et al., 2015). In a vicious cycle, adolescents’ sleep problems lead to behavioral and emotional problems which, in turn, confound sleeping problems (Dahl & Lewin, 2002). Under these pressures, vocational students’ demonstrate irregular sleep habits, reduced sleep duration and quality result in long-term sleep insufficiency, negatively influencing learning. Thus, this study was designed to promote the sleep hygiene among an at-risk group, vocational high school students, due to teenagers’ increased physiological and psychological need for sleep, their relatively irregular sleep patterns, and insufficient sleep. 1.2. Short-comings of traditional sleep hygiene education Sleep hygiene, by definition, refers to habits or behaviors which promote sleep, and was initially developed for use in the treatment of mild to moderate insomnia (Hauri, 1991). In general, sleep hygiene education provides suggestions for environmental factors such as light, noise and temperature as well as behavioral change, such as reduction of caffeine and nicotine intake, increased physical activity, noise reduction, and strategies for relaxation (De Sousa et al., 2007; Sousa, Souza, Louzada, & Azevedo, 2013; Tan, Healey, Gray, & Galland, 2012). The literature on sleep hygiene instruction, however, has demonstrated several drawbacks: 1) classroom-based interventions have tended to focus on cognitive and behavioral approaches, which resulted in insignificant or short-term improvements in students’ sleep; 2) clinical approaches, more often adopted for individuals with chronic sleep disorders or for workplace-based interventions, utilized treatments and clinical devices which are inappropriate for student populations and tended to find sleep hygiene interventions alone to be ineffective; and 3) the variables adopted by sleep hygiene studies often failed to evaluate the effects on students’ academic achievement outcomes associated with changes in sleep, particularly for adolescent populations. The efficacy of classroom-based interventions, which are commonly included as a part of school health curricula, while assumed to benefit the sleep quality of students, lacks sufficient empirical support and warrants deeper evaluation, according to Irish, Kline, Gunn, Buysse, and Hall (2015). In fact, the effectiveness of sleep hygiene instruction, adopting traditional approaches, has often failed to result in behavioral change. For example, Moseley and Gradisar (2009), using a cognitive-behavioral approach, significantly increased students’ in sleep knowledge, but with no changes in sleep or mood. Adopting a traditional cognitive approach to sleep hygiene education, including lectures and quizzes, a study by De Sousa et al. (2007) showed some improvements in sleep regularity, but no changes in sleep quality or daytime sleepiness. In terms of clinical trials, studies which have demonstrated improved sleep outcomes often required invasive procedures or measures. For example, the findings of Taylor, Schmidt-Nowara, Jessop, and Ahearn (2010) found that only the addition of sleep restriction therapy and hypnotic withdrawal to sleep hygiene courses improved sleep outcomes for patients with insomnia. Likewise, Drake et al. (2018) found that sleep hygiene education was ineffective as compared to the clinical interventions of cognitive-behavioral therapy and sleep restriction therapy for patients with chronic insomnia. In fact, a recent systematic review of sleep hygiene education for the treatement of insomnia indicated the relative lack of efficacy for sleep hygiene treatments, with no data available regarding participants’ understanding, adherence, or acceptance of the interventions (Chung et al., 2018). Finally, we considered the lack of studies which demonstrate the impact of improved sleep hygiene on academic outcomes. A 2

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

review of the literature revealed no recent studies which have evaluated the effectiveness of sleep hygiene education classes on ad­ olescents’ academic achievement outcomes. For example, a recent evaluation of sleep hygiene education for Japanese adolescents measured only insomnia symptoms and sleep duration, despite stating emphatically that “sleep hygiene education involves promoting good sleep habits in all aspects of lifestyle and behavior” (Otsuka, Kaneita, Itani, & Tokiya, 2019, p. 1). Studies involving much younger children have been conducted, such as the work of Gruber, Somerville, Bergmame, Fontil, and Paquin (2016), who found improvements in students’ sleep, mathematics and English scores after a community-based sleep education program, based on their previous research linking sleep efficiency with better grades in academic subjects (Gruber et al., 2014). However, these types of studies are far too few, and those which demonstrated significant results generally tended to evaluate pre-adolescents and adopt non-traditional sleep hygiene approaches, such as the community-based intervention designed by Gruber et al. (2016) for young children, and the work of Rey, Guignard-Perret, Imler-Weber, Garcia-Larrea, and Mazza (2020), which involved young children and the involvement of parents. In a meta-analysis of empirical studies adopting sleep hygiene education, Irish et al. (2015) suggested further research into sleep hygiene education should include the following key points: a) the use of a naturalistic and non-clinical setting for evaluation; 2) consideration of the interaction among sleep habits; 3) the inclusion of environmental and social considerations; and 4) the person­ alization of sleep hygiene instruction. In fact, the most current research advocates for the consideration of psychosocial and social factors as they relate to a more holistic conceptualization of adolescent sleep behaviors and its impact on cognition, emotion, and academic performance (Crowley et al., 2018). One study which integrated elements of the recommendations proposed by Irish et al. (2015), such as the inclusion of social considerations and a degree of personalization, is that of Wolfson, Harkins, Johnson, and Marco (2015) who utilized a “Sleep Smart” program. This program, while still emphasizing a teacher-centered approach toward sleep hygiene education, integrated the use of goal-setting, role-plays, self-monitoring, game-based learning, and rewards, which resulted in short term improvements in sleep hy­ giene, sleep duration, behavior, and academic performance, although these improvements were not sustained over time. However, the vast majority of previously designed sleep courses have been largely aimed at changing sleep behavior by focusing on individual behaviors. As a result, there is a lack of studies which have successfully endeavored to promote behavioral change by virtue of group or community power (Reich, Black, & Korobkova, 2014), which is why our present study emphasized the role of social cognition. 1.3. Integrating technology to enhance sleep hygiene instruction The potential benefits provided by technology for naturalistic (classroom-based) sleep hygiene education have received increasing attention over the past years, including the use of actigraphs for evaluating sleep in a non-invasive manner, the development of digital game-based learning for improved motivation, and the use of cloud computing to enhance data collection, storage, and analysis. The use of actigraphs (or mobile activity sensors) for the detection and analysis of sleep parameters allows users to receive feedback on factors such as sleep duration and efficiency (Wang, Yang, Chiang, & Lin, 2011). While the results of self-reported sleep, actigraph measures and clinical measures, such as those provided by polysomnographs may differ, they are considered to measure different aspects of sleep and are recommended to be used in line with the characteristics and needs of the participants (Matthews et al., 2018). In fact the data provided through digital analysis of actigraphy is more reliable because it minimizes interference with a participant’s normal sleep activity (Lubecke & Boric-Lubecke, 2009), as compared to invasive techniques, such as polysomnographs (PSG), with mobile devices found to have sufficient reliability and ease of use (Shambroom, F� abregas, & Johnstone, 2012). Thus, automatic sleep evaluation and monitoring through signals of actigraphs can be easily recorded at school and home through wearable devices (Bianchi, Mendez, & Cerutti, 2010) and, in classroom settings, actigraph data can be used to supplement standard self-report data (Feige et al., 2008). Thus, technology is critical to tracking sleep habits to raise awareness of sleep issues and increase motivation for improved sleep behavior as a means of motivation (Fogg, 2002). Over recent years, with the continuing development of cloud technologies, digital game-based learning has evolved to include more types of online community gaming. As such players are involved with a greater variety of interactions with different people, contexts, and information in a virtual environment which enables players to elevate the position of their team in order to increase the number of available functions or rewards (Wang & Lin, 2009). Online community games have been effectively used to enhance the motivation of adolescent participants in engaging in and maintaining adequate physical activity (Wu, Yang, & Hsieh, 2013). Moreover, recent research on community networks show that e-learning and game-based instructional methods not only increased learning motivation but also helped students achieve behavioral goals (De-Marcos, Domínguez, Saenz-de-Navarrete, & Pag� es, 2014), a finding that suggests the potential of community gaming for enhancing students’ sleep hygiene. Furthermore, the power of cloud computing enables the abovementioned functions of actigraph sensor data collection, analysis, and feedback as well as online community gaming. The use of cloud computing in remote health monitoring, evaluating, and feedback allows for the secure, flexible, and efficient use of mobile sensors (Abawajy & Hassan, 2017; Elhoseny et al., 2018). The implications for classroom-based sleep hygiene courses is evident, in that mobile sleep sensor and mobile community gaming data can be stored and accessed conveniently on cloud servers, which have been increasingly common for educational purposes, since the effective man­ agement of information can improve students’ academic performance and provide scaffolding (Arpaci, 2019), which can potentially lead to improved behavioral change as well (Elbayoudi, Lotfi, & Langensiepen, 2019). However, even the use of technology for evaluating sleep (such as the use of actigraphs), cannot be achieved without appropriate use of pedagogy (Rigney et al., 2015). In a review study, Baron et al. (2018) found that the use of wearable and mobile sleep monitoring technology has largely focused on validation of devices but opportunities exist for observational research and for delivery of behavioral interventions and suggested that future research is needed to determine the uses of technologies in interventions (Baron et al., 2018). 3

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

Thus, the following sections introduce a social-cognitive approach for designing a sleep hygiene educational intervention, and the principles for use of mobile technology based on social-cognitive concepts. 1.4. A social-cognitive approach to sleep hygiene education In light of the recommended sleep research goals proposed by Irish et al. (2015) and the importance of effective technology use to enhance instruction, our study implemented and empirically evaluated the integration of a social-cognitive approach to sleep hygiene instruction. In terms of social-cognitive-based instruction, Glanz, Rimer, and Lewis (2002) highlighted the importance of the reciprocal determinism proposed by social cognitive theory (SCT), emphasizing individual, social, and environmental factors which all interact in influencing human behavior (Clark & Zimmerman, 2014). An SCT approach addresseses the concerns of Irish et al. (2015) by utilizing a naturalistic setting (classroom-based instruction), integrating environmental and social factors, as well as placing an emphasis on both the individual and community as important factors in behavioral change (through an SCT-based approach), approaches that have been rare in the literature on sleep hygiene education. 1.4.1. The influence of sleep on academic achievement Some research has strongly indicated that sleep insufficiency influences learners’ cognitive functioning (Crowley et al., 2018; Curcio, Ferrara, & De Gennaro, 2006; Dewald, Meijer, Oort, Kerkhof, & Bogels, 2010; Ferrie et al., 2011). A recent parallel-group study of East Asian adolescent students, who are reported to have some of the highest levels of sleep deprivation, demonstrated increasingly lower cognitive performance and affect as sleep deprivation continued (Lo, Ong, Leong, Gooley, & Chee, 2016). An increasing number of researchers agree that sleep facilitates working memory as well as memory consolidation, with sleep being an important component for the processing of newly acquired information and for long-term memory storage (De Bruin et al., 2017; Kopasz et al., 2010). Further experimental research has also provided evidence that teenagers with limited sleep duration experienced poorer attention, linguistic creativity, abstract thinking ability, and concept formation ability, with higher volatility in overall cognitive functioning (Beebe et al., 2008). In turn, these physiological and psychological factors were shown to impact learning achievement, with the duration and regularity of sleep positively correlated with academic performance (Onder, Besoluk, Iskender, Masal, & Demirhan, 2014; Short, Gradisar, Lack, & Wright, 2013). One recent study, which evaluated the impact of a social cognitive model of intervention for diet, physical activity, and sleep among obese men found a significant correlation between the socio-structural factors, in terms of setting goals, and achieving improved sleep (Knowlden, Robbins, & Grandner, 2018). This demonstrates that increasing attention is being paid towards the importance of social cognitive factors in behavioral change, which may impact academic achievement indirectly. 1.4.2. A triadic model of SCT-based sleep hygiene integrating technology In this study, each element of the SCT-based triadic model of self-regulated learning, as described by Clark and Zimmerman (2014) was used to design and evaluate an effective intervention for sleep hygiene promotion in order to achieve improvements in sleep quality, emotion, and academic achievement. The three elements of the model, as adapted from Clark and Zimmerman (2014) are described in this section: personal, behavioral, and environmental. First, in terms of personal influences on social-cognitive behavioral change strategies, the efficacy of the participants’ beliefs and behavior change may vary with time, along with their willingness to actively engage in behavioral change. As such, our design follows the suggestions of Lau and Woods (2009) who advocated early-stage behavioral strategies, including the introduction of technology and practice of positive behaviors, which is anticipated to better achieve behavioral change. In fact, recent research by our team has demonstrated how the adoption of technology can enhance both social interaction and collaboration in promoting sleep behavioral change while providing an effective monitoring system for tracking changes in sleep behavior and quality. Additionally, since behavioral change must be based on an individual’s identity and personal goal setting in order to achieve actual positive behavioral change (Mitru et al., 2002), goal setting was required. Second, while social cognition is closely related to personal characteristics and personal goals, key behavioral influences must be fostered. According to Yeh (2007), critical thinking, self-examination, individual perception, and reflective thinking are behavioral elements that can be effectively promoted through the assistance of computer simulations. Finally, this study considered that, in order for a SCT approach to behavioral change to be most successful, goal setting and subsequent behavioral change should be conducted based on a personal recognition of environmental conditions, with a focus on both increasing self-adjustment and confidence as well as engaging in comparison and competition among peers. Some studies, such as Lubans et al. (2012), have evaluated a model of SCT for improving the health behaviors of adolescents, testing the hypothesis that interventions aimed at health-related behavioral change, such as those which encourage self-monitoring, planning and goal setting, promote an individual’s self-efficacy and result in a reduction in barriers to change and the facilitation of healthy behavior. 1.5. Social interdependence and mobile community gaming for sleep hygiene 1.5.1. The influence of community and social interdependence Community can be conceptualized as consisting of a connection of thoughts; wherein commonly-shared ideas are generated through interpersonal interactions (Foster, 1997). Wellman (2001) defined community as an interpersonal network that offers social activities, emotional support, information, belongingness and social identity. Hence, community refers to organizations or groups composed of people who have common goals, purposes, needs or interests in terms of work, environment or life. In terms of health promotion, research has shown that the design of community activities can influence acceptance of new health products at levels 4

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

significantly higher than activities without community activity, echoing the fundings of Norgaard, Sorensen, and Grunert (2014). During childhood and adolescence, the impact of peer interaction and social awareness can be even stronger in influencing intentions and behaviors (Moschis & Churchill, 1978). In fact, a study conducted by Kavalana, Maes, and de Gucht (2010) found that peer in­ fluence is one of the most important factors influencing teenagers’ health behaviors. The social interdependence model (Marcus & Forsyth, 2008) emphasizes the interaction among groups in which members encourage and assist others (Slavin, 1995) in improving and changing health behaviors. 1.5.2. Technology-enhanced mobile technology for developing social interdependence The social interdependence approach has been successful in improving the nutritional intake of adolescents in previous empirical research (Yang, Wang, Tsai, & Wang, 2015; Johnson, Johnson, & Smith, 2007). Social interdependence-based interventions have also been shown to benefit from the integration of technology, such as through online discussions and feedback (Nevgi, Virtanen, & Niemi, 2006). From an affective-social-cognitive approach, the work of Sato (2017) provides support that positive and collaborative attitudes towards learning interactions were fundamental in promoting desired behavioral outcomes. Thus, SCT and social interdependence share similar fundamental assumptions, such as the importance of social collaboration and cognition for effective behavioral change. In terms of Chinese college students, Sheu, Liu, and Li (2017) propose a “modified social cognitive model” in which social interde­ pendence was noted to have both direct and indirect effects of both affective and academic well-being, resulting in greater support and lower stress. Thus the role of SCT through community and the benefits of technology can synergize to create an optimal environment for fostering behavioral change in adolescents. As such, a mobile sleep monitor provided both personal feedback and feedback within an online community for gaming designed for this intervention to ensure that a) no undue pressure was placed on the behavioral change process, b) competition and cooperation or sharing were balanced, in order to improve a sense of community, and c) cognitive strategies were implemented in practice through the use of e-learning and mobile community gaming. Based on previous findings in the area of social cognition, peer/group/community power is expected to enhance individual motivation for continuously engaging in behavioral change (Lin, Su, & Huang, 2012; Marcus, Rossi, Selby, Niaura, & Abrams, 1992), which is in keeping with SCT principles. Based on the role of social interdependence in the context of SCT, community games are one ideal way of combining elements of both collaboration and competition. For example, students can collaborate on meeting their own team goals, while also competing against other teams in the context of a game. Emphasizing the potential of digital games, Ke (2008) found that while cooperation contributed to a social environment which was beneficial for fostering learning motivation and positive emotion, competitive ap­ proaches were more appropriate for encouraging cognition and metacognition, which were essential for learning and adopting behavioral change strategies. Ke (2008) also emphasized the role of instantaneous feedback and the use of points to allow students to gain awards and recognize their ranking among groups, fostering accountability, identity, and promotive interaction. Thus, social games can be optimal for promoting both collaboration and competition simultaneously, as groups of players work cooperatively to meet their behavioral goals in the digital game (Ke, 2008), requiring personal account ability while also competing with other teams to receive achievements and rewards (Slavin, 2009). Consequently, the introduction of gamification elements in this study aimed to reinforce community members’ sense of belonging during game playing (Lin & Lu, 2011; Shin & Shin, 2011) so that users can achieve effective peer learning (Cho, Gay, Davidson, & Ingraffea, 2007) and develop a stronger willingness to participate in and maintain healthy sleep. Previous work by our team (Yang et al., 2015) has highlighted how mobile social games can be designed according to a social interdependence perspective to promote improved nutritional intake. Social interdependence involves players in establishing joint interest and affective investment in attaining goals, fostering a type of promotive interaction, wherein group members encourage and assist others (Slavin, 1995). Thus, taking advantage of the potential benefits of a social interdependence approach (including peer learning, sense of community, and promotive interaction) through the integration of educational technology, such as digital game-based learning and remote sensors for person­ alized feedback, this study seeks to improve vocational students’ sleep and academic outcomes. 1.6. Aims and research questions This study focused on purposively sampling and offered sleep hygiene interventions for vocational students in Taiwan, since large scale surveys have found that 19% of senior high and vocational students work and, as a result, sleep less than 6 h per night (Chang, 2005), with an average sleep time for vocational students of only 5 h per night (Huang, 2009). The integration of technology was critical to this study, as it enabled learners to monitor their sleep behavior, as well as to link this data to the strategies taught in the classroom (for both traditional and SCT-based sleep hygiene instruction). In this study, the use of mobile sensors and a self-designed cloud-based data analysis (the Health promotion cloud system; HPCS) platform (which analyzed students’ sleep habits, including bedtime, sleep onset, sleep duration, and wake time) provided monitoring and feedback on partic­ ipants’ sleep and provided data which served as parameters for participation in mobile community games (MCG). Two experimental groups (one adopting traditional sleep hygiene instruction and the other adopting an SCT-based approach) utilized mobile community gaming in an attempt to create a sense of community and accountability, with both groups hypothesized to implicitly experience the benefits of a social-cognitive approach, while differing in terms of explicit classroom instruction. All groups took advantage of the feedback provided by the mobile sleep sensors. As such, this study adopted an approach based on social cognitive theory (SCT) to attempt to improve the effectiveness of sleep hygiene education, in comparison to traditional sleep hygiene courses (which tend to ignore social and environmental factors and focus on specific behaviors rather than adopting a holistic approach to sleep). Our approach sought to expand upon the evidence that social 5

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

factors are central to behavioral change and to further integrated SCT elements into instruction, bearing in mind the short term benefits evidenced from the social learning model adopted by Wolfson et al. (2015). In terms of the behavioral influences suggested by social cognitive theory (Glanz et al., 2002), and the descriptions provided by Doughty (2011), this study included elements such as reciprocal determinism (taking into account the dynamic relationships among individuals, the environment, and behaviors), self-efficacy, behavioral capacity (including the knowledge and skills required to perform healthy behaviors), and self-control (in terms of setting, implementing, and evaluating goals). The specific elements and activities adopted for this study are highlighted in Table 1 in the Methods section. To examine the effectiveness of the instructional strategy, this research adopted a pretest-posttest quasi-experimental design. Based on three levels of instructional strategy, the research questions of the present study were as follows: 1. Do students receiving different levels of instructional strategy (C: traditional sleep hygiene instruction integrating sleep sensor feedback; E1: traditional sleep hygiene instruction integrating sleep sensor feedback and adopting MCG; and E2: social cognitivebased sleep hygiene instruction integrating sleep sensor feedback and adopting MCG) demonstrate different degrees of improve­ ment in sleep quality, in terms of daytime sleepiness and insomnia? 2. Do students receiving different levels of instructional strategy demonstrate different degrees of improvement in terms of academic achievement as measured by mathematics test scores? 2. Research methods 2.1. Participants Participants were first grade students from an urban vocational high school in Taiwan, with an average age of 16 years. Students entering the school were randomly assigned to different classes and no pre-existing differences among classes were reported by teachers or school administration. Furthermore, since students in this vocational school took a majority of classes from discipline expert teachers rather than homeroom teachers, the three classes selected for the intervention were considered to be relatively ho­ mogeneous. The three classes were randomly assigned as either the comparison group (C; 37 students; receiving a traditional sleep hygiene course and feedback from sleep sensor data), experimental group one (E1; 38 students; receiving a traditional sleep hygiene course with feedback from sleep sensor data and adopting MCG), or experimental group two (E2; 39 students; receiving a social cognitive-based sleep hygiene instruction with sleep sensor feedback and adopting MCG). The sleep intervention courses for the three groups were offered once a week for 50 min, while an additional 50 min per week taught by the instructor using textbook content related to health management for all three classes. All participants completed consent forms and the study passed the university’s Institutional Review Board before the research commenced. 2.2. Material and methods The data collection tools adopted in this study included a daytime sleepiness scale, an insomnia scale, and a mathematics Table 1 Social cognitive elements and related instructional activities for Experimental Group 2. Social Cognitive Element

Description

Instructional Activities

Environment

External factors, including physical and social Thoughts and perceptions about behavior in the context of the environment Dynamic relationships among people, environments, and behaviors Confidence in one’s ability to master a behavior Knowledge and skills to perform a behavior

Brainstorming: Students evaluate the environmental factors which lead to positive or negative sleep hygiene, including their sleep environment and social lives. Self-evaluation essay: Students keep a log of their thoughts and observations concerning their sleep behaviors and how it is influenced by their physical and social environment. Group discussions: Students evaluate strategies in which they can support each other in achieving sleep goals and take proactive steps to improve their sleep environments. Data analysis: Students’ sleep data is evaluated in groups and changes in sleep hours or in daytime sleepiness are observed, bolstering students’ confidence that they can improve. Direct instruction: Strategies and tips for improving sleep and creating a healthy sleep environment are provided by the instructor and discussed by students. Case study: Students are provided with articles that demonstrate the improvements to one’s studies, daily life, and relationships, for example, which can results from good sleep. Debate: Groups debate the pros and cons of changing sleep habits. For example, increased sleep may improve performance, but they may need to sacrifice online gaming. Goal setting: Students set daily and weekly sleep goals and compare their goals to the results which are provided from the mobile sensor data. Results sharing: Students share in groups the results of their sleep outcomes over time, encouraging one another in positive strategies for improving sleep. Awards: Groups are provided with encouragements for meeting their sleep goals. Relaxation techniques: Students are provided with tips on meditation or relaxation before bedtime, in order to reduce their nervousness about meeting sleep goals.

Situation Reciprocal determinism Self-efficacy Behavioral capacity Expectations

Expected outcomes from performing a behavior

Expectancies

Assessments of positive and negative outcomes Setting and achieving goals

Self-control Observational learning Reinforcements Emotional coping responses

Learning from the successes and failures of others Motivators that encourage the behavior Managing emotional stimuli

6

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

achievement test. The instruments are described as follows. 2.2.1. Daytime sleepiness scale The Epworth Sleepiness Scale (ESS; Wang et al., 2003) was adopted for the measurement of daytime sleepiness. This scale included eight items, requiring participants to evaluate their observed frequency of dozing off at school or at home under different scenarios. A five-point Likert scale was used, with 0, 1, 2, 3, and 4 representing “never”, “seldom”, “sometimes”, “frequent” and “always”. Higher scores reflect higher levels of daytime sleepiness, with scores over 16 considered “severe.” This scale has been used extensively to evaluate daytime sleepiness, and has a reliability of Cronbach’s α ¼ 0.74, and test-retest reliability of 0.82. In fact, in order to evaluate daytime sleepiness in non-clinical settings, measures such as the ESS, although dependent upon participants’ subjective evaluation, are necessary. Adding support to the usefulness of the ESS in evaluating daytime sleepiness, a study by Aurora, Caffo, Crainiceanu, and Punjabi (2011) has demonstrated that the ESS was associated clearly with objective (clinical) measures of sleepiness, based on physiological measures and was deemed a suitable and effective predictor of objective sleepiness, particularly when used in non-clinical settings, such as those evaluated in this study. 2.2.2. Insomnia scale The Chinese version of the Athens Insomnia Scale (CAIS) (Soldatos, Dikos, & Paparrigopoulos, 2000) required respondents to evaluate occurrences of insomnia symptoms over the course of one month. The scale included eight items, five items for nighttime symptoms and three items for daytime symptoms. Scoring used a four-point Likert scale, with 0, 1, 2, and 3 representing “no problem”, “slightly delayed”, “markedly delayed” and “very delayed”. Higher scores indicated more severe insomnia. Reliability for the scale ranged from Cronbach’s α ¼ 0.79 to 0.85, and test-retest reliability was 0.86. The use of the CAIS has been validated by empirical studies (Sun, Chiou, & Lin, 2011) to demonstrate concurrent validity with both subjective measures, such as the Pittsburgh Sleep Quality Index-Taiwan (p ¼ .00), and objective measures obtained from physiological data from actigraphs (p ¼ .00), confirming the appropriateness of the scale in terms of consistency, reliability, and validity in both clinical and non-clinical settings (Soldatos, Dikeos, & Paparrigopoulos, 2000). 2.2.3. Academic achievement evaluation This study selected mathematics test scores as indicators to evaluate academic achievement. Mathematics has been proposed as one of the most powerful predictors of later learning and academic success (Duncan et al., 2007). A key component to success in math­ ematics is the ability to understand highly logical and systematic relationships among numerals and mathematical procedures (Geary, 2013). Additionally, in school-aged children from seven to 11 years of age, sleep has been associated with better grades in mathematics (Gruber et al., 2014). In addition, our team determined that mathematics was the optimal subject for evaluating academic achievement for a number of other reasons, including: 1) mathematics tends to be a high-stakes subject for entrance examinations or graduation requirements and, as such, is an appropriate subject to evaluate the potential impact of improved sleep hygiene for vocational students:

Fig. 1. Experimental flow of the study. 7

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

2) answers to mathematics problems can be objectively evaluated, with questions provided that directly match students’ learning content. For the exam, two teachers with more than 10 years of experience in mathematics education were invited to develop questions. Topics covered factors, multiples, polynomial equation and cubic functions, including 10 multiple choice questions and 5 calculation questions. The total score for each test was 100. The alternate-form reliability of the pretest and posttest in the pilot study exceeded 0.80, and had expert validity and reliability. In terms of validity, the tests were designed by experienced mathematics teachers who have over ten years of experience, resulting in expert validity. The content of the examinations were aligned to the objectives and competency indicators from Taiwan’s Ministry of Education national curriculum for mathematics education (content validity) and discussion were held with both teachers and researchers in order to determine the appropriateness of the test items (primarily in terms of difficulty and discrimination) in order to address face validity. 2.3. Research design 2.3.1. Flow of the experimental design The study lasted 12 weeks (See Fig. 1). At the first and twelfth week, pretests and posttests were conducted. Introductions to the intervention were also provided and parents were asked to sign a consent form before the intervention began. The first week was also used to familiarize students with the Health Promotion Cloud System (HPCS) and how to obtain feedback on their sleep from wearable mobile devices. During the second week, basic sleep knowledge content was taught for all classes. From the third week to the sixth week, the first stage of social cognitive-based instruction was provided for E2. During this time, C and E1 students received traditional sleep hygiene instruction and received sleep outcome feedback from their mobile sleep monitor sensors. Traditional sleep hygiene instruction included lectures, including topics related to environmental factors (light, noise and temperature) and social factors (such as work and lifestyle-related factors). Furthermore, class discussions were held in order to promote students’ behavioral change, with topics ranging from how to caffeine and nicotine intake, to increasing physical activity, and adopting strategies for relaxation. Between the seventh week and the eleventh week, both E1 and E2 students used data from their mobile health sensors to engage in a mobile community game via the HPCS, which is described in a following section. During this time, comparison group students continued to study sleep hygiene using the previous instructional methods. It should be noted that while the E2 group utilized SCTbased instructional content, participants in the E1 and C groups also potentially benefited from the overall SCT-based design of the intervention by receiving individual sleep feedback from the mobile sleep sensors, and discussing their sleep progress and conditions during class. Thus, some SCT-based principles were present, even for “traditional” instruction, through the integration of technology for sleep feedback. The three elements of our model, as adapted from Clark and Zimmerman (2014) are described in this section: personal, behavioral, and environmental. In terms of personal elements, a significant component of our intervention involved leading participants to focus on attainable changes, also in the context of the changes they observe from their peers, following suggestions from Lau and Woods (2009) regarding the role of training in behavioral change. Therefore, in this study, the instructional strategy for behavioral change first emphasized assisting students in setting goals and reviewing their progress. In addition, goal setting was adapted in response to the results ach­ ieved, as suggested by (Mitru et al., 2002). As such, students were guided in the use of several approaches: self-regulated learning strategies (such as predicting and verifying goals that are likely to be achieved in the future); perceptions of self-efficacy and per­ formance (such as correcting the method used for goal attainment by assessing the gap between their current situation and their goal); and a sustained commitment to health goals (by understanding the importance of the goals and seeking physical and social resources) in order to seek to achieve successful behavioral change (Zimmerman, 1990). In terms of behavioral influences, in addition to providing explicit instruction on social cognitive elements or strategies, such as self-efficacy and behavioral capacity, our study adopted computer-based community games in order for students to apply these strategies for achieving self-regulation and behavioral change, receiving immediate feedback on their individual and group progress in order to develop the behavioral skills of self-monitoring, self-judgment, and self-reaction, as emphasized by Clark and Zimmerman (2014). Finally, in terms of environmental factors, this interpretation of SCT took into account individual expectations and capabilities, such as the ability to cope with change, gain confidence, and utilize self-control. Furthermore, a holistic application of SCT in the classroom must also consider the central role of the social context wherein students learn from observing and interacting with others, forming reciprocal relationships, and evaluating personal goals in comparison with the goals and behaviors of others (Glanz et al., 2002). A holistic approach adopting reciprocal relationships for setting and evaluating both personal and group behavioral goals is a unique element and potential contribution of the present study. In sum, as applied in this research, the SCT-based approach for sleep behavioral change consisted of several elements which were facilitated through the use of technology (Marcus & Forsyth, 2008). Learners acquired sleep knowledge, goals, and beliefs for developing positive sleep behaviors, augmented by the content of the mobile community game (personal influences). The course increased students’ awareness of their own sleep insufficiency, achieving an understanding of the importance of sleep hygiene, and emphasizing self-efficacy skills, utilizing mobile sleep sensors for immediate feedback (behavioral influences). Students improved their understanding of the influences of sleep on self, others, and the environment, as well as seeking support from social resources, such as peers, parents, or teachers, through the Health Promotion Cloud System (environmental influences).

8

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

2.3.2. Health promotion cloud system (HPCS) Current studies of behavioral change have shown a trend towards computer-enhanced behavioral change strategies (Levesque et al., 2011). Therefore, this study designed an online community framework using HPCS to combine sleep-related course materials and instructional content with the development of a practical mobile system for sleep data recording, evaluation, and monitoring (Lin, Yang, & Wang, 2012). Mobile health recorders objectively recorded students’ in-class and at-home physiological signals, which were converted into variables which were used as inputs for algorithms. Through analysis of these signals, users were finally presented with objective information on sleep quality, such as sleep duration and onset. These data were then used as elements which enabled learners to compete in a team-based cloud network game aimed at transforming cognitive change into behavioral change (E1 and E2), through the practice of healthy sleeping habits which would assist students in game play. Additionally, all three groups received feedback on their sleep through the system. In terms of the mobile device, its validity and reliability in measuring sleep outcomes has been verified by our research team. The accuracy of discriminating sleeping and waking states was greater than 90%. Its size is similar to a watch and, once worn on the student’s wrist, the sensor automatically recorded all activities for an entire day (see Fig. 2). Subsequently, via a mobile application, students uploaded relevant data to the HPCS (see Fig. 3). After the recorded parameter values were uploaded to the server, the server further used algorithms to analyze students’ sleep efficiency, duration, and posture change. Analysis also took into account users’ physical information such as height and weight. 2.3.3. Comparison and experimental group 1 course content Course content for the C and E1 groups focused on sleep hygiene and sleep-related knowledge, including habits to avoid before bedtime, the role of food in sleep, evaluation of sleep-wake cycles, stages of sleep, and other health-related topics. In addition, the two groups were able to view results for their sleep quality and sleep duration obtained from the mobile sleep monitor sensors. Instruction for these two groups was traditional, but with additional emphasis on sleep hygiene, and the inclusion of mobile sensor data analysis and discussion (which involved some elements of SCT theory, despite an overall traditional approach to instruction). 2.3.4. Experimental group 2 course content The instructional strategies for E2 were based on social cognitive theory (Glanz et al., 2002), and the descriptions provided by Doughty (2011). These are presented in Table 1. In addition to these specific activities, students also adopted the data from the mobile sleep monitor sensors to evaluate their progress and complete course activities. An overview of the theoretical design principles for the SCT-based course design was introduced in Section 1.5. 2.3.5. Mobile community game play for E1 and E2 The community game for this study, entitled“Sweet Dreams in Taiwan,” required students to work in teams to meet individual and group goals. The design of the game was based on social-interdependence principles and included elements of both between-group competition and within-group collaboration (Ke, 2008). The appearance of the interface resembled a board game, in that students were required to pass through stations while circling the game board. If all members uploaded data from their mobile sleep monitor sensor or achieved preset goals for each day (such as meeting a minimum of 7 h of sleep for that night), the team could move forward one step, requiring students to exercise personal accountability to achieve goals and receive rewards (Slavin, 1995). The ranking of each team was shown on the interface. In order to integrate knowledge learned during class, particularly during the instruction of

Fig. 2. Appearance and functions of the mobile sleep monitor sensor. 9

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

Fig. 3. Framework of sleep promotion cloud system (HPCS).

cognitive change strategies, the game involved stages where sleep knowledge taught during class was reviewed and tested through small quizzes, adopting elements of social networking, such as online communication and collaboration, mentioned by Cho et al. (2007) for meeting goals and monitoring of group progress, thereby seeking to build students’ relationships and achieve peer learning. Seven sleep activity checkpoints (Fig. 4) and seven quizzes (Fig. 5) were provided during the course of the game, containing content relevant to sleep hygiene, such as which beverages to avoid before bedtime, which are also advocated by traditional approaches to behavioral change (De Sousa et al., 2007. Only after all group members read the sleep knowledge pop-ups which appeared during game play, could the team pass through the checkpoint and move forward. Quizzes were also used and were based on the sleep knowledge pop-ups. All students in the team were required to pass the test then they could move to the next checkpoint, involving the use of reciprocal determinism to achieve behavioral change, similar to the recommendations of Glanz et al. (2002). Each quiz consisted of five questions, and providing four out of five correct answers was required to pass the checkpoint. In the event of failure, the system randomly selected five different questions for the player to answer, providing emotional support, by allowing players to avoid failure and meet their goals through additional efforts, thereby taking advantage of technology for immediate feedback and revision (Wellman, 2001). m. Fig. 6 shows information provided for each team during the game. The purpose of this page was to provide current position and ranking information for the teams, thereby increasing challenge, interest, and competition (Ke, 2008). In order to increase the 10

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

Fig. 4. Sleeping tips: adopted from the sleep hygiene course content.

Fig. 5. Sleeping quiz: including five multiple choice questions based on the sleep hygiene course content and the blue station tips. (For inter­ pretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

enjoyability of the game, the team could also discuss the use of cards with special functions. For instance, turtle cards could be used to make the opponent go back five steps, or plane cards could be used to help the team move forward two steps. These cards are illustrated in Fig. 7. Fig. 8 illustrates personal information for the game, which was designed to provide a personalized interface and encourage interaction with other members in order to increase mutual encouragement among peers. Along with personalized sleep feedback, rankings are shown for other team-members. Furthermore, in order to encourage students to use the mobile sleep monitor sensor and regularly upload data, students gained five gold coins if they both uploaded daily data and met the sleep standards. Gold coins could then be used in exchange for awards. These technology-enabled features of the community game were designed to promote interaction, mutual encouragement, and affective investment, which are elements of social interdependence (Slavin, 1995) central to our design. In sum, for the mobile community game presented in this study, members of each team monitored the sleep status of themselves and their teammates, engaging in promotive interaction, with the team leader reminding members about sleep problems and encouraging classmates to upload data in order to gain access to the reward system, which offered actual gifts to students at the end of the intervention (Cho et al., 2007; Lin & Lu, 2011; Slavin, 1995). Elements of social cognitive instruction were adopted here, including self-control, observational learning, and reinforcements (Lubans et al., 2012; Shin & Shin, 2011). Teachers were also able to check the 11

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

Fig. 6. Group information pane for the game: a) daily sleep goal progress, b) current position, laps, and accumulated points, c) leaderboard of all groups, and d) suggested cards for exchange.

Fig. 7. Cards that can be used by groups during game play to increase interest.

12

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

Fig. 8. Individual information pane for the game: a) daily sleep goal progress, b) coins gained, sleep time and efficiency, c) leaderboard for the group, and d) suggested awards to be exchanged.

sleep conditions of students before class, in order to provide individualized feedback during class and ask teams with positive per­ formance to share their keys to success in achieving their goals (Wolfson et al., 2015). Thus, the game design was based on the tenets of Social Cognitive Theory, a social-interdependence approach, and game-based theory in order to facilitate students’ awareness of sleep issues and develop sleep hygiene behaviors within a social context which supported their behavioral change (Clark & Zimmerman, 2014). 2.4. Data analysis The data were first evaluated for outliers or missing data. Due to some forms being incomplete, these were removed from eval­ uation. The remainder of the data were analyzed using one-way ANOVAs, with change scores (posttest scores minus pretest scores) used as dependent variables in order to compare the effectiveness of the three levels of intervention. The use of difference or change scores was deemed appropriate since, although the groups had been randomly assigned to one of the conditions, intact groups were used (Jamieson, 2004). Differences on the pretest were, therefore, accounted for by the difference score, which was analyzed by ANOVA for differences among groups. Since there were existing pretest differences, if Levene’s statistic for equality of variances was significant, post-hoc analysis was conducted using the Games-Howell Nonparametric Post-Hoc test. Furthermore, correlation analysis and regression analysis were used to evaluate the relationships among insomnia, daytime sleepiness, and the academic achievement outcome for mathematics. 3. Results 3.1. Daytime sleepiness One-way ANOVA was used to analyze differences among change scores for students’ total daytime sleepiness and insomnia scores for the three groups. According to the results, students from each group demonstrated a decrease in daytime-sleepiness from the pretest Table 2 ANOVA analysis of daytime sleepiness (Change scores) for the three groups. Group C E1 E2

N 37 38 39

Pretest

Posttest

Diff (Post - Pre)

M

SD

M

SD

13.92 17.42 19.10

5.14 5.35 5.02

12.03 14.05 13.95

5.06 4.80 5.04

M 1.89 3.37 5.15

C: Comparison group; E1: Experimental group one; E2: Experimental group two. 13

SD

F

p

ƞ2

Post hoc

1.71 2.56 4.34

10.60

.00

.16

E2＜C E1＜C

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

to the posttest. Mean pretest, posttest, and change scores for all groups are listed in Table 2. Results show that differences among the three groups in terms of change scores were significant (F(2, 111) ¼ 10.60, p ¼ .00, ƞ2 ¼ 0.16). According to post-hoc comparisons, E2 and E1 improved significantly more than C, while there were no significant differences in the ANOVA model between E1 and E2, despite the fact that E2 demonstrated a larger decrease in daytime sleepiness overall. However, paired comparisons between E1 and E2 did demonstrate a significant difference (F ¼ 4.80; p ¼ .03; ƞ2 ¼ 0.06), with E2 outperforming E1 on adjusted post-test means, and both E1 (F ¼ 8.56; p ¼ .01; ƞ2 ¼ 0.11) and E2 (F ¼ 18.22; p ¼ .00; ƞ2 ¼ 0.20) outperforming C. The use of t-tests to compare the improvements found significant differences for both daytime sleepiness for all three groups, with p ¼ .00 and η2 ranging from 0.56 to 0.64. 3.2. Insomnia For the insomnia scale, students of each group had lower posttest scores than the pretest scores. Mean pretest, posttest scores, and change scores for all groups are listed in Table 3. ANOVA results show that differences in change scores among the three groups were significant (F(2, 111) ¼ 8.03, p ¼ .00, ƞ2 ¼ 0.13). According to post-hoc comparisons, E2 and E1 both improved significantly more C, while there were no significant differences between E1 and E2 on the adjusted posttest despite the fact that E2 demonstrated a larger decrease in insomnia overall. The use of ttests to compare the improvements found significant differences for both daytime sleepiness for all three groups, with p ¼ .00 and η2 ranging from 0.33 to 0.60. 3.3. Academic achievement In order to evaluate the impact of different instructional interventions on academic achievement, mathematics test scores were chosen for analysis. In order to reduce the burden of multiple tests, researchers use students’ monthly exam scores as the dependent variable, since these exams were developed by qualified instructions (see the Methods section). One-way ANOVA was used to analyze changes in students’ academic achievement scores for each of the three groups. Mean pretest, posttest, and change scores for all groups are listed in Table 4. With respect to mathematics achievement, posttest scores for all groups were higher than the pretest scores. Mean pretest, posttest, and change scores for all groups are listed in Table 5. Results show significant differences in change scores among the three groups (F(2, 111) ¼ 24.79, p ¼ .00, ƞ2 ¼ 0.31). According to posthoc comparisons, E2 improved significantly more than both E1 and C, while C outperformed E1 in terms of positive change between the pretest and posttest. This test also demonstrates the advantage of the E2 treatment on sleep quality, thereby influencing academic achievement. In fact, paired t-tests showed that all groups improved significantly in terms of daytime sleepiness and insomnia (p ¼ .00), while E2 (p ¼ .00; ƞ2 ¼ 0.81), C (p ¼ .00; ƞ2 ¼ 0.46) and E1 (p ¼ .02; ƞ2 ¼ 0.14) demonstrated significant within-group change in math achievement. 3.4. Correlation and regression analysis As mentioned above, improvements in sleep were expected to be correlated with students’ academic achievement. Correlation analysis revealed a significant correlation between daytime sleepiness and insomnia change scores (r ¼ .32, p ¼ .00). Furthermore, daytime sleepiness change scores were negatively correlated with the change scores for mathematics achievement (r ¼ 0.28, p ¼ .00), suggesting that students improvements in daytime sleepiness (i.e., lower scores for daytime sleepiness) were associated with positive changes in mathematics achievement. In order to further evaluate the overall influence of the sleep outcome variables, as well as the change scores for daytime sleepiness and insomnia were regressed on the mathematics change scores (the predictor variable). The results of linear regression analysis are included in Table 5. R2 value of 0.08 was observed, indicating that the contribution of the sleep change scores explained for 8% of the variance in mathematics change scores. According to Cohen (1988), R2 of 0.02 is considered to have a weak effect size, while an R2 of 0.13 is considered a medium effect size for regression analysis. As such, the contribution of sleep change to the change in academic achievement for mathematics may not be large, but it does explain part of the variance in the change scores. The model was significant (F(2, 111) ¼ 4.78, p ¼ .01) and the β coefficient for daytime sleepiness was 0.27 (t ¼ 2.80, p ¼ .00). The negative coefficient indicates that decreases in daytime sleepiness, resulting in a negative change score, are associated with improvements in math achievement. Table 3 ANOVA analysis of insomnia (Change scores) for the three groups. Group C E1 E2

N 37 38 39

Pretest

Posttest

Diff (Post - Pre)

M

SD

M

SD

5.03 5.95 7.56

3.53 4.12 3.80

4.16 4.18 5.21

3.30 3.54 3.87

M 0.86 1.76 2.36

C: Comparison group; E1: Experimental group one; E2: Experimental group two. 14

SD

F

p

ƞ2

Post hoc

1.25 1.46 2.06

8.03

.00

.13

E2＜C E1＜C

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

Table 4 ANOVA analysis of mathematics test scores (Change scores) for the three groups. Group C E1 E2

N 37 38 39

Pretest

Posttest

Diff (Post - Pre)

M

SD

M

SD

M

SD

F

p

ƞ2

Post hoc

53.03 67.55 39.79

19.54 22.17 17.05

64.62 72.71 64.74

11.32 12.74 10.15

11.59 5.16 24.95

12.75 12.94 12.09

24.79

.00

.31

E2＞C＞E1

C: Comparison group; E1: Experimental group one; E2: Experimental group two. Table 5 Linear regression analysis (Dependent variable: mathematics change scores; Independent variables: daytime sleepiness and insomnia change scores). Variable

B

SE

β

t

p

Constant Daytime sleepiness (change scores) Insomnia (change scores)

9.30 1.21 -.30

2.18 .43 .83

-.27 -.03

4.28 2.80 -.36

.00 .00 .72

R ¼ 0.28, R2 ¼ 0.08, F(2, 111) ¼ 4.78, p ¼ .01.

4. Discussion 4.1. Sleep hygiene 4.1.1. Daytime sleepiness The findings for daytime sleepiness suggest that while the use of HPCS for monitoring and comparing sleep habits significantly improved daytime sleepiness for all participants, the addition of mobile community games, based on social-interdependence (Marcus & Forsyth, 2008), were significantly more successful in enhancing students’ sleep hygiene strategies and promoting effective and behavioral change. This result echoes that of our previous research in the use of social-interdependence theory-based social games for nutrition promotion (Yang, Wang, Tsai, & Wang, 2015) and suggests that social games can take advantage of players’ members’ sense of belonging during game play (Lin & Lu, 2011; Shin & Shin, 2011) and effective peer learning (Cho et al., 2007) in order to achieve positive behavioral change. The usefulness of the mobile community games for utilizing positive peer influence to improve health behavioral outcomes echoes the findings of Kavalana et al. (2010). The advantage of further providing social cognitive-based courses specifically tailored to students’ sleep hygiene needs was not firmly demonstrated by this result, although it is noted that the E2 treatment did make larger improvements in daytime sleepiness. However, since all students, even those taking traditional courses, showed significant improvement, with large effect sizes, in terms of daytime sleepiness, the effectiveness of the feedback provided by the mobile sensors was for improving sleep outcomes is suggested (Arpaci, 2019; Bianchi et al., 2010; Fogg, 2002). 4.1.2. Insomnia Similar to the results of daytime sleepiness, the adoption of mobile community games was deemed critical for improving students’ insomnia, as significantly greater improvement was noted for E1 and E2. Additionally, all three groups experienced improvement in insomnia, suggesting that the cloud system, which offered sleep monitoring and feedback may have been effective through engaging participants in self-monitoring. Furthermore, through the mobile community game, students were able to monitor their own sleep situation and compare it with their own goals (as with Lubans et al., 2012) and the results of their peers simultaneously (Glanz et al., 2002), by using mobile sensor data which did not interfere with their normal sleep (Lubecke & Boric-Lubecke, 2009). As a result, the scores for E1 and E2 both improved, indicating the potential effect of community and self-monitoring, as reported by (Nevgi et al., 2006). However, E2 also received tailored social cognitive-based sleep hygiene strategies, which further promoted students’ use of goal setting and actual implementation of strategies (Norgaard et al., 2014), bringing greater benefits by increasing students’ self-perception, self-efficacy, and reciprocal determinism (Doughty, 2011; Glanz et al., 2002), as well as a richer sense of social support (Sheu et al., 2017), although their change was not statistically significant than the E1 group. This suggests that the most salient factor in improving insomnia was the mobile community game. The HPCS enabled learners to examine their sleep improvement, in comparison to others and their own personal goals, echoing the suggestions of Wolfson et al. (2015) and Gruber et al. (2016), who found that social learning-based interventions can impact students’ sleep hygiene as well as academic achievement. The findings suggest that the resulting behavioral change may have been more sig­ nificant than that which could be offered only by the instruction provided through social cognitive-based sleep hygiene courses without technological assistance. These results are in agreement with the findings of (Yang (2012)), which suggest that technology, including digital games, can develop students’ problem solving abilities and increase learning motivation. Furthermore, vocational students frequently suffer from insufficient sleep, which negatively influences learning (Gerber et al., 2015). The social cognitive model, both for the mobile community game and the SCT-based sleep hygiene instruction evaluated by this study, was designed to motivate vocational students to examine their sleeping habits (empowered by mobile sensors and a cloud-based 15

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

health promotion system), helping them set and achieve goals for sleep self-improvement. While it may be impossible to change certain lifestyle parameters (such as part-time jobs or academic loading), by interrupting the “vicious cycle” proposed by Dahl and Lewin (2002), it is hoped that improvements to sleep behaviors can alleviate the emotional and psychological impact in students’ lives. 4.2. Academic achievement Considering previous studies on the link between the quality of sleep and academic achievement (Crowley et al., 2018; Onder et al., 2014; Short et al., 2013), we believe that a positive relationship between sleep outcomes and academic achievement should be explained by the fact that students’ sleep quality is related to their physical health, mental health, and variables such as concentration and attention (Crowley et al., 2018; Curcio et al., 2006; Dewald et al., 2010; Ferrie et al., 2011). However, the lack of a strong cor­ relation between the variables of daytime sleepiness and sleep hygiene make this interpretation difficult to support. This seems to coincide with the difficulties highlighted by Irish et al. (2015) in strongly linking sleep hygiene interventions to concrete sleep out­ comes. Undoubtedly, elements of the instructional intervention impacted students’ sleep, as section 4.1 has described. However, the impact of the intervention on the observed improvements in mathematics achievement for all groups may have indirect effects on students’ academic achievement, either through other variables of sleep (including duration, consistency, and quality), such as in a recent study of college students using wearable sleep sensors (Okano, Kaczmarzyk, Dave, Gabrieli, & Grossman, 2019) which found a relationship between sleep duration, consistency, and quality on academic achievement. However, while our study found that only 8% of academic achievement could be explained by sleep variables, the results of Okano et al. (2019) reported a 25% explained variance. 4.3. Overall findings and interpretations Overall improvement suggests that the inclusion of the mobile sensor with feedback on sleep quality was sufficient to provide improvement in both sleep and academic outcomes for all students, even though, by itself, it was not as effective as treatments integrating mobile community games. The collection of data through mobile, non-invasive devices (like a smart watch) meets the recommendations of Irish et al. (2015) for a naturalistic evaluation of sleep, as opposed to the clinical studies which have been done. As such, the data provided was more reliable and minimized interference normal sleep activity (Lubecke & Boric-Lubecke, 2009), resulting in both useful and reliable feedback for self-monitoring and behavioral modification (Lubans et al., 2012), further supporting the use of technology in promoting health behavioral change (De-Marcos et al., 2014; Patel, Asch, & Volpp, 2015). The major improvement in E2 students’ academic achievement may potentially be due to a transfer of learning effect from the principles taught using SCT-based instruction (based on design principles from Clark & Zimmerman, 2014 Doughty, 2011, and Glanz et al., 2002) and other aspects of students’ lives, including mood, concentration, and study skills. Mood, particularly through digital game-based activities has been shown to be related to the use of SCT approaches (such as Nguyen et al., 2018). Aspects of concentration have been evaluated from the SCT perspective, with Bembenutty, White, and DiBenedetto (2016) suggesting K-12 educators adopt SCT instructional procedures to enhance self-efficacy and self-regulation. In terms of study skills, Devi, Khandelwal, and Das (2017) reflect on the importance of SCT in designing instructional activities to improve learning within a technology-enhanced environment. As such, some instructional techniques from the SCT curriculum that could impact mood, concentration, and study skills are as follows: for students’ mood, activities include relaxation techniques and awards, while those impacting concentration could include case-study analysis, debating, and goal-setting, and those influencing study skills might include brainstorming, self-evaluation essays and group discussions. The results indicate that the most significant factor in improving students’ sleep and, theoretically, impacting students’ academic achievement was the use of the mobile community game. In fact, under both the conditions of traditional sleep hygiene instruction and SCT-based sleep hygiene instruction, students in both E1 and E2 demonstrated significant improvements over the comparison group (which did not play the mobile community game) in terms of daytime sleepiness and insomnia. This finding provides further support for the use of technology in promoting health behavioral change, such as the work of De-Marcos et al. (2014) which suggests that community networks adopting e-learning and game-based instructional methods increase both learning motivation and empower students in achieving behavioral change goals. The social interdependence model of cooperative learning (Marcus & Forsyth, 2008), which emphasizes interaction among groups in which members encourage and assist others (Slavin, 1995) in improving and changing health behaviors, was a key to the design of the mobile community game. Other major features included between-group competition and within-group collaboration for creating challenge, interest, and competition (Ke, 2008), individual contributions to meeting group goals and receive rewards, resulting in accountability (Slavin, 1995), elements of social networking for community building (Cho et al., 2007), and the synergy of community and technology for immediate feedback and revision (Wellman, 2001). Finally, since both the instructional treatment and the mobile community game were based on Social Cognitive Theory, we believe that the intervention has successfully addressed the concerns of Irish et al. (2015) by utilizing a naturalistic setting (classroom-based instruction), integrating environmental and social factors (through the mobile community game as well as the course content), as well as placing an emphasis on both the individual and community as important factors in behavioral change, using the social-interdependence model for game play based on mobile sleep sensor data. The adoption of mobile devices was utilized in order to enable students to engage in the approaches recommended by Zimmerman (1990) for successful behavioral change: self-regulation; perceptions of self-efficacy and performance, and a sustained commitment to health goals (all of which were facilitated by the daily sleep feedback provided by the mobile sensors and the HPCS). 16

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

5. Conclusions and suggestions 5.1. Implications and contributions After a thorough review of the literature, it is clear that very few studies have successfully implemented sleep-promotion in­ struction (Cain, Gradisar, & Moseley, 2011) or increasing sleep knowledge without impacting other parameters (Kira, Maddison, Hull, Blunden, & Olds, 2014; Moseley & Gradisar, 2009). As such, a social-cognitive approach was adopted, in order to take into consid­ eration not only personal and behavioral influences, but also the social context of learning (Clark & Zimmerman, 2014; Glanz et al., 2002; Knowlden et al., 2018). This study demonstrates how the use of technology to provide mobile community gaming can integrate elements of competition and collaboration to situate the learner in a social context (De-Marcos et al., 2014; Ke, 2008), offering op­ portunities for both offering and receiving encouragement (Wang & Lin, 2009), while providing the challenge of accountability within groups, as recommended by (Slavin, 1995). This finding is in agreement with the work of Maume (2013) who stated that, in the context of an analysis of adolescent sleep, “health is facilitated by individuals becoming embedded in multiple networks of positive associ­ ations with key actors in their lives” (p. 509). Thus, this study outlines and demonstrates the effectiveness of mobile community gaming designed to develop the sense of group or community power (Reich et al., 2014) as well as the social cognitive skills of self-regulation necessary for behavioral change (Clark & Zimmerman, 2014). In this way, community-enhanced individual motivation for continuously engaging in behavioral change was promoted (Lin et al., 2012; Marcus et al., 1992), which is in keeping with SCT principles. The contribution of this paper is an approach for integrating technology for sleep hygiene promotion through a variety of features, based on social interdependence and social cognitive-based sleep hygiene concepts, as summarized in the discussion section and in Table 1. Furthermore, this paper illustrates how mobile sensor data can be integrated into instructional activities to provide students a chance to examine their own and peers’ sleep conditions so as to achieve more successful behavioral change and learning (De-Marcos et al., 2014; Lau & Woods, 2009). 5.2. Limitations and recommendations This study intentionally selected vocational students as participants, since they are known to have problems with sleep insuffi­ ciency (Chang, 2005; Huang, 2009). Thus, integrating technology into courses emphasizing aspects of social cognitive theory, including personal, behavioral, and environmental factors (Clark & Zimmerman, 2014) can potentially encourage adolescents to develop healthy sleep habits to ensure the sufficient sleep quality and duration which is critical for enhancing the behavioral and academic outcomes for vocational students (Mitru et al., 2002). Further application of mobile sensors, in particular, is recommended for students in other contexts. Given prior research with Australian secondary students (Cain et al., 2011; Moseley and Gradisar, 2009) which found that students are happy to participate in homework based and school-based sleep behavior experiments, we believe that the use of technology to provide mobile health monitoring and mobile community gaming could be applied to other contexts and other countries. As such, further evaluation of the SCT-based behavioral change strategies and mobile community gaming for students of other ages, in other types of schools, or in other cultures would be beneficial to the literature. Furthermore, evaluation of academic achievement could be evaluated in alternative ways, such as through the measurement of students overall GPA or sleep hygiene-related knowledge acquisition. Other outcomes, such as engagement, collaboration, sense of community, and others, could be evaluated through the use of observations, student self-reported data, and qualitative analysis. Another consideration is the use of technology in the context of sleep behavioral change. In this study, a positive effect was found for students who played the mobile community game and who used sleep sensor data to evaluate their sleeping condition. Research by Wentworth and Middleton (2014) suggested that some negative effects of technology use may also impact learning, but failed to find strong links between cellphone and computer use and students’ GPA, nevertheless recommending larger samples to evaluate the true effects of technology on learning. Likewise, this study failed to find a significant difference between the E1 and E2 condition, although higher change scores were noted for E2, which adopted our SCT-based curriculum design. Thus, future research should consider evaluating an experimental condition in which the social cognitive sleep hygiene strategies were utilized (with the use of sensor data analysis), without the use of mobile community games in order to evaluate the effectiveness of the SCT instructional intervention alone. In addition, a more appropriate experimental model might consider to alternating the instructional strategies among groups over a longer duration, such as a full school year, so that the effects of the treatments can be more accurately assessed. Such a model should also include evaluation of delayed or long term effects. Furthermore, the potential of technology, particularly in terms of mobile devices, is an issue to be considered in future studies. Some studies have adopted mobile applications, rather than wearable devices, to track sleep behaviors and outcomes (eg. Shirazi et al., 2013). This type of treatment may have the unavoidable side effect of increased screen time exposure before bed, which can be disruptive to sleep (Hale & Guan, 2015). While our intervention used a wearable device, similar to a watch, to track sleep, care should be taken in future research to avoid unnecessary exposure to mobile technology before or during bedtime hours. In this study, we adopted self-report measures for daytime sleepiness and insomnia. While objective data was processed by the HPCS system in order to provide feedback to students and for use as parameters for mobile community gaming, these measures were not evaluated as dependent variables in this study. Daytime sleepiness and insomnia scales were used to evaluate students’ perfor­ mance in the classroom, since it was considered that each individual’s personal sleep goals, as well as sleep needs, may differ, particularly as this research was based on a social cognitive approach (Maes & Gebhardt, 2000). Thus, the use of subjective scales for sleep quality, as measured by daytime classroom self-reports, was considered more appropriate and less likely to be biased by 17

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

individuals’ variance in sleep onset or duration which are related to personal and developmental differences. However, future studies could evaluate biometric measures of sleep duration or sleep quality, based on mobile sensors, in order to obtain an objective eval­ uation of adolescent sleep quality or sleep patterns after a similar intervention. Finally, we should consider how the use of a collaborative-competitive social interdependence approach may have impacted students’ sleep. First, the competitive element may have led to a potential increase in arousal or stress associated with placing goals on sleep duration. Romyn et al. (2016) found that, for athletes wearing mobile sensors, the condition of competition (as compared to training) resulted in a negative effect on sleep efficiency. Although learners were provided with direct instruction on strategies for healthy sleep hygiene and behaviors, such as avoiding caffeine and setting manageable goals, it is possible that some participants may have experienced an increase in arousal which may have negatively impacted sleep. However, a recent meta-analysis of adolescent sleep factors indicates only a small and insignificant effect for pre-sleep worry and arousal in terms of sleep duration and latency, despite the fact that a small delay in sleep onset may occur (Bartel, Gradisar, & Williamson, 2015). Furthermore, as a recent study has noted, an increased understanding of the role of interdependence in successfully achieving goals is necessary, and that when goals are interdependent, such as in the case of the groups in this study, goal success can be better achieved (Fitzsimons & Finkel, 2015). Nevertheless, further evaluation of the role of the social interdependence approach in behaviors that may be negatively impacted by arousal, such as sleep, is warranted. CRediT authorship contribution statement Jeen-Shing Wang: Conceptualization, Software, Resources, Data curation. Jeffrey Hugh Gamble: Writing - original draft, Writing - review & editing. Ya-Ting Carolyn Yang: Supervision, Investigation, Project administration, Writing - original draft, Formal analysis, Funding acquisition. Acknowledgments This study is supported in part by the Ministry of Science and Technology of the Republic of China under grant MOST 105-2628-S006-001-MY. Special thanks to Dr. Wen-Lung Wu for his kind assistance. References Abawajy, J. H., & Hassan, M. M. (2017). Federated internet of things and cloud computing pervasive patient health monitoring system. IEEE Communications Magazine, 55(1), 48–53. Arpaci, I. (2019). A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education. Computers in Human Behavior, 90, 181–187. Aurora, R. N., Caffo, B., Crainiceanu, C., & Punjabi, N. M. (2011). Correlating subjective and objective sleepiness: Revisiting the association using survival analysis. Sleep, 34(12), 1707–1714. Baron, K. G., Duffecy, J., Berendsen, M. A., Mason, I. C., Lattie, E. G., & Manalo, N. C. (2018). Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep. Sleep Medicine Reviews, 40, 151–159. Bartel, K. A., Gradisar, M., & Williamson, P. (2015). Protective and risk factors for adolescent sleep: A meta-analytical review. Sleep Medicine Reviews, 21, 72–85. Beebe, D. W., Fallone, G., Godiwala, N., Flanigan, M., Martin, D., Schaffner, L., et al. (2008). Feasibility and behavioral effects of an at home multi night sleep restriction protocol for adolescents. Journal of Child Psychology and Psychiatry, 49, 915–923. Bembenutty, H., White, M. C., & DiBenedetto, M. K. (2016). Applying social cognitive theory in the development of self-regulated competencies throughout K-12 grades. In Psychosocial skills and school systems in the 21st century (pp. 215–239). Cham: Springer. Bianchi, A. M., Mendez, M. O., & Cerutti, S. (2010). Processing of signals recorded through smart devices: Sleep-quality assessment. IEEE Transactions on Information Technology in Biomedicine, 14(3), 741–747. Brand, S., Kirov, R., Kalak, N., Gerber, M., Schmidt, N. B., Lemola, S., & Holsboer-Trachsler, E. (2016). Poor sleep is related to lower emotional competence among adolescents. Behavioral Sleep Medicine, 14(6), 602–614. Cain, N., Gradisar, M., & Moseley, L. (2011). A motivational school-based intervention for adolescent sleep problems. Sleep Medicine, 12, 246–251. Chang, L. Y. (2005). Taiwan education panel survey: First wave: Student questionnaires. Taipei, Taiwan: Academia Sinica Institute of Sociology. Retrieved from: http:// www.teps.sinica.edu.tw/TEPSNews/TEPS~News_032.pdf. Cho, H., Gay, G., Davidson, B., & Ingraffea, A. (2007). Social networks, communication styles, and learning performance in a CSCL community. Computers & Education, 49(2), 309–329. Chung, K. F., Lee, C. T., Yeung, W. F., Chan, M. S., Chung, E. W. Y., & Lin, W. L. (2018). Sleep hygiene education as a treatment of insomnia: A systematic review and meta-analysis. Family Practice, 35(4), 365–375. Clark, N. M., & Zimmerman, B. J. (2014). A social cognitive view of self-regulated learning about health. Health Education & Behavior, 41(5), 485–491. Cohen, J. E. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates. Crowley, S. J., Wolfson, A. R., Tarokh, L., & Carskadon, M. A. (2018). An update on adolescent sleep: New evidence informing the perfect storm model. Journal of Adolescence, 67, 55–65. Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic performance. Sleep Medicine Reviews, 10(5), 323–337. https://doi.org/ 10.1016/j.smrv.2005.11.001. Dahl, R., & Carskadon, M. A. (1995). Sleep and its disorders in adolescence. In M. H. Kryger, T. Roth, & W. C. Dement (Eds.), Principles and practice of sleep medicine in the child (pp. 19–27). Philidelphia: WB Saunders & Co. Dahl, R. E., & Lewin, D. S. (2002). Pathways to adolescent health: Sleep regulation and behavior. Journal of Adolescent Health, 31, 175–184. De Bruin, E. J., van Run, C., Staaks, J., & Meijer, A. M. (2017). Effects of sleep manipulation on cognitive functioning of adolescents: A systematic review. Sleep Medicine Reviews, 32, 45–57. De Sousa, I. C., Araújo, J. F., & de Azevedo, C. V. M. (2007). The effect of a sleep hygiene education program on the sleep-wake cycle of Brazilian adolescent students. Sleep and Biological Rhythms, 5(4), 251–258. De-Marcos, L., Domínguez, A., Saenz-de-Navarrete, J., & Pag�es, C. (2014). An empirical study comparing gamification and social networking on e-learning. Computers & Education, 75, 82–91. Devi, B., Khandelwal, B., & Das, M. (2017). Application of Bandura’s social cognitive theory in the technology enhanced, blended learning environment. International Journal of Applied Research, 3(1), 721–724.

18

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

Dewald, J. F., Meijer, A. M., Oort, F. J., Kerkhof, G. A., & Bogels, S. M. (2010). The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: A meta-analytic review. Sleep Medicine Reviews, 14(3), 179–189. Doughty, P. (2011). Contextual considerations for behavior change: Intervention/method selection. In G. G. Gilbert, R. G. Sawyer, & E. McNeill (Eds.), Creating strategies for school and community health (pp. 47–96). Sudbury, MA: Jones & Barlett. Drake, C. L., Kalmbach, D. A., Arnedt, J. T., Cheng, P., Tonnu, C. V., Cuamatzi-Castelan, A., et al. (2018). Treating chronic insomnia in postmenopausal women: A randomized clinical trial comparing cognitive-behavioral therapy for insomnia, sleep restriction therapy, and sleep hygiene education. Sleep, 42(2), zsy217. Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., et al. (2007). School readiness and later achievement. Developmental Psychology, 43(6), 1428. Elbayoudi, A., Lotfi, A., & Langensiepen, C. (2019). The human behaviour indicator: A measure of behavioural evolution. Expert Systems with Applications, 118, 493–505. Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. (2018). A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems, 86, 1383–1394. Feige, B., Al-Shajlawi, A. N. A. M., Nissen, C., Voderholzer, U., Hornyak, M., Spiegelhalder, K., & Riemann, D. (2008). Does REM sleep contribute to subjective wake time in primary insomnia? A comparison of polysomnographic and subjective sleep in 100 patients. Journal of Sleep Research, 17(2), 180–190. Ferrie, J., Shipley, M., Akbaraly, T., Marmot, M., Kivimaki, M., & Singh-Manoux, A. (2011). Change in sleep duration and cognitive function: Findings from the Whitehall II Study. Sleep, 34(5), 565–573. Fitzsimons, G. M., & Finkel, E. J. (2015). Goal interdependence. Current Opinion in Psychology, 1, 10–13. Fogg, B. J. (2002). Persuasive technology: Using computers to change what we think and do. San Francisco, CA: Morgan Kaufmann. Foster, D. (1997). Community and identity in the electronic village. In D. Porter (Ed.), Internet culture (pp. 23–37). London: Routledge. Geary, D. C. (2013). Early foundations for mathematics learning and their relations to learning disabilities. Current Directions in Psychological Science, 22(1), 23–27. Gerber, M., Lang, C., Feldmeth, A. K., Elliot, C., Brand, S., Holsboer-Trachsler, E., et al. (2015). Burnout and mental health in Swiss vocational students: The moderating role of physical activity. Journal of Research on Adolescence, 25(1), 63–74. Glanz, K., Rimer, B. K., & Lewis, F. M. (2002). Health behavior and health education. Theory, research and practice. San Fransisco: Wiley & Sons. Goldstein, A. N., & Walker, M. P. (2014). The role of sleep in emotional brain function. Annual Review of Clinical Psychology, 10, 679–708. Gruber, R., Somerville, G., Bergmame, L., Fontil, L., & Paquin, S. (2016). School-based sleep education program improves sleep and academic performance of schoolage children. Sleep Medicine, 21, 93–100. Gruber, R., Somerville, G., Enros, P., Paquin, S., Kestler, M., & Gillies-Poitras, E. (2014). Sleep efficiency (but not sleep duration) of healthy school-age children is associated with grades in math and languages. Sleep Medicine, 15(12), 1517–1525. Hale, L., & Guan, S. (2015). Screen time and sleep among school-aged children and adolescents: A systematic literature review. Sleep Medicine Reviews, 21, 50–58. Hauri, P. J. (1991). Sleep hygiene relaxation therapy and cognitive intervention. In P. J. Hauri (Ed.), Case studies in insomnia (pp. 65–84). New York: Plenum Publishing. Hirshkowitz, M., Whiton, K., Albert, S. M., Alessi, C., Bruni, O., DonCarlos, L., & Neubauer, D. N. (2015). National Sleep Foundation’s sleep time duration recommendations: Methodology and results summary. Sleep Health, 1(1), 40–43. Huang, Y. L. (2009). Sleep quality of adolescents in vocational high School (Unpublished master’s thesis). Taipei, Taiwan: National Yang Ming University. Huang, Y. S., Wang, C. H., & Guilleminault, C. (2010). An epidemiologic study of sleep problems among adolescents in north Taiwan. Sleep Medicine, 11(10), 1035–1042. Irish, L. A., Kline, C. E., Gunn, H. E., Buysse, D. J., & Hall, M. H. (2015). The role of sleep hygiene in promoting public health: A review of empirical evidence. Sleep Medicine Reviews, 22, 23–36. James, E., Gaskell, M. G., Weighall, A., & Henderson, L. (2017). Consolidation of vocabulary during sleep: The rich get richer? Neuroscience & Biobehavioral Reviews, 77, 1–13. Jamieson, J. (2004). Analysis of covariance (ANCOVA) with difference scores. International Journal of Psychophysiology, 52(3), 277–283. Johnson, D. W., Johnson, R. T., & Smith, K. (2007). The state of cooperative learning in postsecondary and professional settings. Educational Psychology Review, 19(1), 15–29. Kavalana, T. V., Maes, S., & de Gucht, V. (2010). Interpersonal and self-regulation determinants of healthy and unhealthy eating behavior in adolescents. Journal of Health Psychology, 15, 44–52. Ke, F. (2008). Computer games application within alternative classroom goal structures: Cognitive, metacognitive, and affective evaluation. Educational Technology Research & Development, 56(5–6), 539–556. Kira, G., Maddison, R., Hull, M., Blunden, S., & Olds, T. (2014). Sleep education improves the sleep duration of adolescents: A randomized controlled pilot study. Journal of Clinical Sleep Medicine, 10(7), 1–6. Knowlden, A. P., Robbins, R., & Grandner, M. (2018). Social cognitive models of fruit and vegetable consumption, moderate physical activity, and sleep behavior in overweight and obese men. Health Behavior Research, 1(2). https://doi.org/10.4148/2572-1836.1011. Kopasz, M., Loessl, B., Hornyak, M., Riemann, D., Nissen, C., Piosczyk, H., et al. (2010). Sleep and memory in healthy children and adolescents–a critical review. Sleep Medicine Reviews, 14(3), 167–177. Lau, S. H., & Woods, P. C. (2009). Understanding the behavior changes in belief and attitude among experienced and inexperienced learning object users. Computers & Education, 52(2), 333–342. Levesque, D. A., Van Marter, D. F., Schneider, R. J., Bauer, M. R., Goldberg, D. N., Prochaska, J. O., et al. (2011). Randomized trial of a computer-tailored intervention for patients with depression. American Journal of Health Promotion, 26(2), 77–89. Lin, K. Y., & Lu, H. P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152–1161. Lin, C. H., Su, J. D., & Huang, L. H. (2012a). Effects of group intervention and nutrition education on the anthropometry in elementary overweight children. Chia-nan Annual Bulletin, 38, 116–123. Lin, C. W., Yang, Y. T. C., Wang, J. S., & Yang, Y. C. (2012b). A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation. IEEE Transactions on Information Technology in Biomedicine, 16(5), 991–998. Lo, J. C., Ong, J. L., Leong, R. L., Gooley, J. J., & Chee, M. W. (2016). Cognitive performance, sleepiness, and mood in partially sleep deprived adolescents: The need for sleep study. Sleep, 39(3), 687–698. Lo, I. C., & Wu, Y. Y. (2007). Relationships of physical activity and sleep quality. Elementary Education, 48(1), 56–62. Lubans, D. R., Plotnikoff, R. C., Morgan, P. J., Dewar, D., Costigan, C., & Collins, C. E. (2012). Explaining the dietary intake in adolescent girls from disadvantaged secondary schools: A test of social cognitive theory. Appetite, 58(2), 517–524. Lubecke, V. M., & Boric-Lubecke, O. (2009). January). Wireless technologies in sleep monitoring. In Radio and wireless symposium, 2009. RWS’09. IEEE (pp. 135–138). IEEE. Maes, S., & Gebhardt, W. (2000). Self-regulation and health behavior: The health behavior goal model. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 343–368). San Diego, CA: Academic Press. Marcus, B. H., & Forsyth, L. H. (2008). Motivating people to be physically active (2nd ed.). Champaign, IL: Human Kinetics. Marcus, B. H., Rossi, J. S., Selby, V. C., Niaura, R. S., & Abrams, D. B. (1992). The stages and processes of exercise adoption and maintenance in a worksite sample. Health Psychology, 11, 386–395. Matthews, K. A., Patel, S. R., Pantesco, E. J., Buysse, D. J., Kamarck, T. W., Lee, L., et al. (2018). Similarities and differences in estimates of sleep duration by polysomnography, actigraphy, diary, and self-reported habitual sleep in a community sample. Sleep Health, 4(1), 96–103. Maume, D. J. (2013). Social ties and adolescent sleep disruption. Journal of Health and Social Behavior, 54(4), 498–515. Mitru, G., Millrood, D., & Mateika, J. (2002). The impact of sleep on learning and behavior in adolescents. Teachers College Record, 104(4), 704–726.

19

Computers & Education 151 (2020) 103812

J.-S. Wang et al.

Moschis, G. P., & Churchill, G. A. (1978). Consumer socialization. A theoretical and empirical analysis. Journal of Marketing Research, 15, 599–609. Moseley, M., & Gradisar, M. (2009). Evaluation of a school-based intervention for adolescent sleep problems. Sleep, 32(3), 334–341. Nevgi, A., Virtanen, P., & Niemi, H. (2006). Supporting students to develop collaborative learning skills in technology-based environments. British Journal of Educational Technology, 37(6), 937–947. Nguyen, H. V., Huang, H. C., Wong, M. K., Yang, Y. H., Huang, T. L., & Teng, C. I. (2018). Moderator roles of optimism and weight control on the impact of playing exergames on happiness: The perspective of social cognitive theory using a randomized controlled trial. Games for Health Journal, 7(4), 246–252. Norgaard, M. K., Sorensen, B. T., & Grunert, K. G. (2014). Social and individual determinants of adolescents’ acceptance of novel healthy and cool snack products. Computers & Education, 83, 226–235. Okano, K., Kaczmarzyk, J. R., Dave, N., Gabrieli, J. D., & Grossman, J. C. (2019). Sleep quality, duration, and consistency are associated with better academic performance in college students. NPJ Science of Learning, 4(1), 1–5. Onder, I., Besoluk, S., Iskender, M., Masal, E., & Demirhan, E. (2014). Circadian preferences, sleep quality and sleep patterns, personality, academic motivation and academic achievement of university students. Learning and Individual Differences, 32, 184–192. Otsuka, Y., Kaneita, Y., Itani, O., & Tokiya, M. (2019). A school-based sleep hygiene education program for adolescents in Japan: A large-scale comparative intervention study. Sleep and Biological Rhythms, 1–10. https://doi.org/10.1007/s41105-019-00240-1. Palmer, C. A., & Alfano, C. A. (2017). Sleep and emotion regulation: An organizing, integrative review. Sleep Medicine Reviews, 31, 6–16. Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable devices as facilitators, not drivers, of health behavior change. JAMA, 313(5), 459–460. Reich, S. M., Black, R. W., & Korobkova, K. (2014). Connections and communities in virtual worlds designed for children. Journal of Community Psychology, 42(3), 255–267. Rey, A. E., Guignard-Perret, A., Imler-Weber, F., Garcia-Larrea, L., & Mazza, S. (2020). Improving sleep, cognitive functioning and academic performance with sleep education at school in children. Learning and Instruction, 65, 101270. Rigney, G., Blunden, S., Maher, C., Dollman, J., Parvazian, S., Matricciani, L., et al. (2015). Can a school-based sleep education programme improve sleep knowledge, hygiene and behaviours using a randomised controlled trial. Sleep Medicine, 16(6), 736–745. Romyn, G., Robey, E., Dimmock, J. A., Halson, S. L., & Peeling, P. (2016). Sleep, anxiety and electronic device use by athletes in the training and competition environments. European Journal of Sport Science, 16(3), 301–308. Sanford, L. D., Yang, L., Wellman, L. L., Liu, X., & Tang, X. (2010). Differential effects of controllable and uncontrollable footshock stress on sleep in mice. Sleep, 33(5), 621–630. Sato, M. (2017). Interaction mindsets, interactional behaviors, and L2 development: An affective-social-cognitive model. Language Learning, 67(2), 249–283. Shambroom, J. R., F� abregas, S. E., & Johnstone, J. (2012). Validation of an automated wireless system to monitor sleep in healthy adults. Journal of Sleep Research, 21 (2), 221–230. Sheu, H. B., Liu, Y., & Li, Y. (2017). Well-being of college students in China: Testing a modified social cognitive model. Journal of Career Assessment, 25(1), 144–158. Shin, D. H., & Shin, Y. J. (2011). Why do people play social network games? Computers in Human Behavior, 27(2), 852–861. Shirazi, A. S., Clawson, J., Hassanpour, Y., Tourian, M. J., Schmidt, A., Chi, E. H., et al. (2013). Already up? Using mobile phones to track and share sleep behavior. International Journal of Human-Computer Studies, 71, 878–888. Short, M. A., Gradisar, M., Lack, L. C., & Wright, H. R. (2013). The impact of sleep on adolescent depressed mood, alertness and academic performance. Journal of Adolescence, 36, 1025–1033. Slavin, R. (1995). Cooperative learning: Theory, research and practice. Needham Heights, MA: Simon & Schuster Company. Slavin, R. E. (2009). Cooperative learning. In G. McCulloch, & D. Crook (Eds.), International encyclopedia of education (pp. 161–178). Abington, UK: Routledge. Soldatos, C. R., Dikeos, D. G., & Paparrigopoulos, T. J. (2000). Athens insomnia scale: Validation of an instrument based on ICD-10 criteria. Journal of Psychosomatic Research, 48(6), 555–560. Song, W. C., & Gao, S. F. (2000). Sleep disorders in children and adolescents. Formosan Journal of Medicine, 4(6), 681–686. Sousa, I. C., Souza, J. C., Louzada, F. M., & Azevedo, C. V. M. (2013). Changes in sleep habits and knowledge after an educational sleep program in 12th grade students. Sleep and Biological Rhythms, 11(3), 144–153. Sun, J. L., Chiou, J. F., & Lin, C. C. (2011). Validation of the Taiwanese version of the Athens Insomnia Scale and assessment of insomnia in Taiwanese cancer patients. Journal of Pain and Symptom Management, 41(5), 904–914. Tan, E., Healey, D., Gray, A. R., & Galland, B. C. (2012). Sleep hygiene intervention for youth aged 10 to 18 years with problematic sleep: A before-after pilot study. BMC Pediatrics, 12, 189. Taylor, D. J., Schmidt-Nowara, W., Jessop, C. A., & Ahearn, J. (2010). Sleep restriction therapy and hypnotic withdrawal versus sleep hygiene education in hypnotic using patients with insomnia. Journal of Clinical Sleep Medicine, 6(2), 169–175. Wang, B. C., Li, H. Y., Jhuang, M. L., Huang, Y. S., Chen, Y. H., Ciou, G. L., et al. (2003). Reliability and validity of the Chinese version of the Epworth sleepiness scale. Taiwanese Journal of Psychiatry, 17(1), 14–22. Wang, W. Y., & Lin, J. W. (2009). Growth dynamics of Taiwan online game industry. Journal of Information Management, 16(2), 25–60. Wang, J., Yang, Y. T. C., Chiang, W., & Lin, C. (2011). Using a wearable physical activity sensor system for sleep/wake states detection. Sleep and Biological Rhythms. 9 (4), 378. Wellman, B. (2001). Physical place and cyberplace: The rise of networked individualism. In L. Keeble, & B. D. Loader (Eds.), Community informatics: Shaping computermediated social telations (pp. 227–252). London: Routledge. Wentworth, D. K., & Middleton, J. H. (2014). Technology use and academic performance. Computers & Education, 78, 306–311. Whalley, K. (2014). Sleep: Young sleep. Nature Reviews Neuroscience, 15(6), 353–361. Wheaton, A. G., Olsen, E. O., Miller, G. F., & Croft, J. B. (2016). Sleep uration and njury-related risk behaviors among high school students: United States, 2007–2013. Morbidity and Mortality Weekly Report, 65, 337–341. https://doi.org/10.15585/mmwr.mm6513a1. Wolfson, A. R., Harkins, E., Johnson, M., & Marco, C. (2015). Effects of the young adolescent sleep smart program on sleep hygiene practices, sleep health efficacy, and behavioral well-being. Sleep Health, 1(3), 197–204. Wu, W. C. I, Yang, Y. T. C., & Hsieh, T. Y. (2013). An application of cloud physical activity promotion system on high school female students’ physical activity. International Journal of e-Education, e-Business, e-Management and e-Learning, 3(2), 155–157. Yang, Y. T. C. (2012). Building virtual cities, inspiring intelligent citizens: Digital games for developing students’ problem solving and learning motivation. Computers & Education, 59(2), 365–377. Yang, Y. T. C., Wang, C. J., Tsai, M. F., & Wang, J. S. (2015). Technology-enhanced game-based team learning for improving intake of food groups and nutritional elements. Computers & Education, 88, 143–159. Yeh, Y. C. (2007). Aptitude-treatment interactions in preservice teachers’ behavior change during computer-simulated teaching. Computers & Education, 48(3), 495–507. Zimmerman, B. J. (1990). Self-regulating academic learning andachievement: The emergence of a social cognitive perspective. Educational Psychology Review, 2, 173–201.

20