Learner perceptions versus technology usage: A study of adolescent English learners in Hong Kong secondary schools

Accepted Manuscript Learner perceptions versus technology usage: A study of adolescent English learners in Hong Kong secondary schools Cynthia Lee, Alexander Seeshing Yeung, Kwok Wai Cheung PII:

S0360-1315(19)30005-3

DOI:

https://doi.org/10.1016/j.compedu.2019.01.005

Reference:

CAE 3512

To appear in:

Computers & Education

Received Date: 3 August 2017 Revised Date:

8 January 2019

Accepted Date: 9 January 2019

Please cite this article as: Lee C., Yeung A.S. & Cheung K.W., Learner perceptions versus technology usage: A study of adolescent English learners in Hong Kong secondary schools, Computers & Education (2019), doi: https://doi.org/10.1016/j.compedu.2019.01.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Authors:

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Kwok Wai Cheung Department of Computer Studies Hong Kong Baptist University Waterloo Road, Kowloon Tong Hong Kong [email protected]

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Alexander Seeshing Yeung Institute for Positive Psychology and Education Australian Catholic University 25A Barker Road, Strathfield, NSW 2135 Locked Bag 2002, Strathfield NSW 2135 [email protected]

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Cynthia Lee (Corresponding author) School of Education and Languages The Open University of Hong Kong Good Shepherd Street Ho Man Tin, Kowloon Hong Kong [email protected]

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Learner Perceptions Versus Technology Usage: A Study of Adolescent English Learners in Hong Kong Secondary Schools Abstract

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There is a wealth of research investigating the predictive power of learners’ attitudes towards technology, perceived usefulness of technology and efficacy. However, the associations between these learner factors and the actual application of technology for individualized and collaborative school-related learning activities in a specific domain and in the Asian context are not adequately discussed. This paper contributes to the literature by attempting to investigate such correlations, and treating individualized and collaborative applications as separate entities, with particular reference to Chinese adolescent learners of English in Hong Kong. An analysis of 193 questionnaires

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completed by adolescent English learners aged 13-16 in three Hong Kong secondary schools supported the differentiation of actual application for individualized and collaborative school-related English learning activities. While the adolescents’ attitudes, self-efficacy (familiarity with technology) and perceptions towards technology use were positive, attitude was related to use of technology for individualized learning purposes, and self-efficacy was related to perceived usefulness of technology for English learning. Their perceived usefulness of technology and actual application behavior for school-related learning tasks were not commensurate with each other.

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Older adolescent learners tended to favor more technology as a useful tool for English learning. Gender effects, however, were negligible. The study points to the significance of understanding both learner perceptions and relations with actual application of technology use for school-related English learning activities.

Introduction

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Keywords: Chinese English learners, learner factors, technology adoption, adolescents, attitude, efficacy

With the advancement of technology, a wide range of technological resources such as computer, email, video-conferencing, online chat, wikis and the Internet have been adopted for educational activities to enhance learning (e.g., Golonka et al., 2014; Liu et al., 2010, Tsai & Tsai, 2010). Likewise, in the field of English language education, technology facilitates interactions with peers or collaborations (Adnan, 2017; Lee, 2016; Lee & Markey, 2014), and enhances individual language skill practice (Chu et al., 2017; Mak & Coniam, 2008). Although technology is a tempting alternative to promote language learning and are pedagogically in vogue (Thomas & Reinders, 2010), it is important to understand what factors influence technology use in general. Personal factors such as attitude, perceived usefulness of technology and self-efficacy of learners have been examined (Cai, Fan, & Du, 2017; Celik & Yesilyurt, 2013; Lai, Wang, & Lei, 2012; Shank &

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Cotton, 2014). However, what is lacking is a clear understanding of the contributions of these personal factors to technology use and actual adoption in a specific domain in a particular learning environment.

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This study contributes to the literature by examining the three personal factors (attitude towards the value of technology, self-efficacy in using technology, and perceived usefulness of technology) on the one hand, and the relationships between perceived usefulness of technology and actual adoption of technology on the other in English learning in Hong Kong. Technology use, in the present study, refers to the use of technological tools (computers, the Internet, smartphones, tablets and digital devices) and technology skills (grammar check, online search engines) for language learning. Application of technology for language learning could be for individualized learning that involves only the individual learner with an intended learning goal (e.g., to look for information from the

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Internet for an assignment), or for interactive or collaborative learning that includes one party (e.g., a teacher) or more than one party (e.g., classmates) to exchange information or discuss an idea or a project. A list of these commonly used tools and skills was generated from known resources available in families reported in public resources (Census and Statistics Department, 2018), the government’s subject curriculum guide (Curriculum Development Council 2017c), as well as suggestions from adolescent users. The participants of this study were a group of adolescent secondary school learners in a specific domain – English language learning. Adolescent language

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learners, who are a major group of technology users in the contemporary world, deserve attention. This paper first reviews the three learner factors that might influence technology use, namely self-efficacy (Bandura, 1997), attitude-behavior relations (Ajzen & Fishbein, 1977), and perceived usefulness of technology adoption (Davis, 1989). Second, it discusses if there are differences

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between male and female adolescents in those three aspects. On the basis of the review, the paper presents the research questions, definitions of terms, relevant background information of the present study, and the design of a questionnaire survey. Then, it describes the data collection procedures and statistical analysis. Finally, it discusses the salient features, and implications for computer-assisted English language education. Learner Factors and Technology Use

Learners’ actions and behaviors are influenced by many factors, some of which are related to learners’ themselves such as attitude (Ajzen & Fishbein, 1977), perception and self-efficacy (Bandura, 1982; 1997). A person’s attitude is important because it can predict his or her response to an object, performance or behavior (Ajzen & Fishbein, 1977). If a person has a positive attitude towards an object, he or she will intend or plan to perform a favorable act or behavior related to that object. Moreover, a person’s perceived self-efficacy or belief in his or her capability can determine how he or she executes courses of actions and confidence in face of demands, failures and anxiety (Bandura, 1982; 1997). One’s self-efficacy is related to one’s mastery experience such that success in performance increases one’s self-efficacy while increased competence beliefs such as

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self-efficacy promotes further mastery potential (Chen & Yeung, 2015; Marsh et al., 2017). The concept of self-efficacy has been extended from one’s belief about self and ability which is social in nature (Bandura, 1982; 1997) to one’s belief in having the ability to use information and communication technology (Rohatgi, Schere & Hatlevik, 2016). One’s positive attitude towards

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technology and strong belief in having the ability to use technology will influence one’s choice to become more familiar with technology, and how to make best use of it. As reported in the literature of technology-supported learning, it is known that a learner’s positive attitude (Lai, Wang, & Lei, 2012), strong belief in one’s ability to use technology (Hatlevik et al., 2018) or familiarity with technology (Shank & Cotton, 2014), and perceived usefulness of technology (Cheung & Vogel, 2013; Clark et al., 2009; Lee & Lehto, 2013; Liu et al., 2010; Saadé & Bahli, 2005) are powerful predictors for technology use. These factors also determine how much effort a learner will put in learning through technology (Celik & Yesilyurt, 2013; Hatlevik et al., 2018; Howard, Ma & Yang,

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2016; Moos & Azevedo, 2009). In this study, students’ familiarity with technology is assumed to be a factor affecting their belief in their ability and attitude to use technology and perceived usefulness of technology. Understanding learners’ beliefs, perceptions and attitudes towards technology use according to gender enables us to better understand learning processes and help teachers make informed decisions (Reychav, McHaney & Burke, 2017). Analyzing 50 research articles on the issue of

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self-efficacy, attitudes (belief and affect) and gender from 1997 to 2014 conducted in North America, Asia and Europe, Cai, Fan, and Du (2017) found that both male and female learners had demonstrated positive attitudes (affect, beliefs and self-efficacy) towards different types of technology tools in general. Their positive attitudes predicted their acceptance and adoption of

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technology, though males showed more favorable attitude, particularly in beliefs, and greater self-efficacy or familiarity with technology than females. In terms of gender differences in attitudes across regions, the differences tended to be larger among North American students, but smaller among Asian and European students. However, Shashanni and Khalili (2001), and Vekiri and Chronaki (2008) found that female adolescents have a lower level of perceived self-efficacy and less positive attitude or value beliefs towards technology than male adolescents in their studies. Research suggests that gender gap, though exists, has gradually reduced (Papstergiou, 2008; Tsai & Tsai, 2010; TØmte & Hatlevik, 2011). While perceived usefulness of technology may carry a similar meaning whether in a specific domain or in general terms, the actual application of technology for learning may be quite different. As argued by Clark et al. (2009, p.56), it is essential “to understand a great deal more about what it is young people do with their technologies”. Tsai and Tsai (2010) found that the male Chinese adolescents in Taiwan used the Internet for individual learning purposes (i.e., exploration purposes such as searching, reading and downloading online information), whereas the female Chinese adolescents used it for communication purposes (reading online messages, chatting and discussing some issues with friends). In another study, Zhao et al. (2010) found from their survey analysis that

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the Internet self-efficacy of junior and high school students in China was positively related to home Internet accessibility. However, they reported that support from peers and school had a greater effect on Internet self-efficacy than that from home. 3. Addressing the Research Gap

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The brief review shows that recent studies (e.g., Ajzen & Fishbein, 1977; Cheung & Vogel, 2013; Clark et al., 2009; Hatlevik et al., 2018; Saadé & Bahli, 2005; Shank & Cotton, 2014) primarily analyzed the relations between attitudes, perceptions and technology use in general. In spite of the fact that there are numerous studies on the relations, the correlation between learner factors and learners’ actual behaviors in the face of individualized or collaborative learning purposes in a specific domain are not adequately explored. Further to this, the number of studies in the Chinese

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context is relatively small (Tsai & Lin, 2004; Tsai & Tsai, 2010; Zhou, 2014). Therefore, a major contribution of this research is the potential to better understand how the three learner factors (i.e., attitudes, self-efficacy, perceived usefulness of technology) are related to the outcome variables (i.e., what and how they use technology) among adolescent learners in a specific domain – English language in the Chinese context. Adolescent learners who are a key group of technology users in the contemporary world deserve greater attention (Sundqvist & Sylvén, 2014). By identifying the correlations between the factors of attitude, perceived self-efficacy and usefulness of technology (in

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the general sense and for individualized and collaborative learning), and the outcome variables, language educators will have a clear direction in designing specific technology-supported tasks to reach target outcomes, and promote English language teaching to adolescents through technology. In the present study, technology use refers to the use of a range of technology tools for both

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individualized and collaborative English learning defined in the government’s curriculum guide of the region (See 5.1.1 – English Language and Technology Education in Hong Kong Secondary Schools for details). While individualized and collaborative learning are inevitably interrelated and therefore cannot be taken as dichotomous or mutually exclusive, by addressing the two types of learning separately, we will be able to relate each to different learner perceptions. The knowledge will inform us which perception may facilitate which specific type of learning through technology, and hence which perceptions and activities to promote to obtain best learning outcomes. In this study, self-efficacy is glossed as a learner’s ability to use and familiarity with the use of information and computer technology related skills (e.g., word processing, spell check, online chat rooms etc.) for school-related English learning activities. Attitude refers to thoughts and beliefs in the value of technology for school-related English learning activities. 4. Research Questions The present study aims to answer the following research questions. RQ1: Among the learners’ personal factors (attitude, general usefulness of technology, self-efficacy,

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usefulness of technology for individualized and collaborative purposes), which ones are the strongest correlates for the adoption of individualized and collaborative applications of technology for English language learning at schools?

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Rationale: Attitude is an important factor for reasoned actions and planned behaviors. Research that investigates learner attitudes and technology use primarily focuses on tertiary students and their general beliefs or behaviors. Perceived usefulness and self-efficacy in regards to computers are two related concepts to attitude. An understanding of how these constructs are related to adolescent language learners’ adoption of technology for English learning activities will enable educators to focus interventions on the most relevant constructs for obtaining results in promoting related behaviors.

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RQ2: Do adolescent learners differentiate between the application of technology for individualized and collaborative English learning purposes at schools, and does age or gender matter? Rationale: As previously mentioned, perceptions towards the usefulness of technology may vary with learners’ use for individualized and collaborative learning purposes, and little is known about adolescent learners’ perceptions and actual application. In this study, application of technology for individualized learning refers to activities that mainly involve only the individual learner who uses technology to achieve his or her English learning purposes, whereas collaborative learning refers to activities that include one party or more than one party (e.g., a teacher or a few classmates) on a

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forum or a chat group to share items or discuss a project. Although gender difference has been reduced, it is still worth investigating if male and female adolescent learners would perceive and apply technology use in a similar way.

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RQ3: Is adolescent students’ perceived usefulness of technology for individualized and collaborative school-related English learning activities commensurate with their actual adoption of technology for these respective activities? Rationale: Research studies usually investigate the relations or impact of learners’ perceived usefulness regarding technology use. Inadequate attention is given to the relations between perceived usefulness and actual adoption of the technology for learning. 5. The Study 5.1 Background to the Present Study 5.1.1 Technology Education (TE) and English Language Education (ELE) in Hong Kong schools and the participating schools The goals of developing subject knowledge, technology literacy and generic skills (collaborative skills, collaborative problem solving skills, group work skills, IT skills etc.) has been recommended

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by the Hong Kong Special Administrative Region Government (HKSARG) to all public sector schools i through the Secondary Education Curriculum Guide (2017a), English Language Education Key Language Area (ELDKLA) Curriculum Guide (2017b) and Technology Education Key Language Area(TEKLA)Curriculum Guide (2017c). The HKSARG provides six years of free

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and universal primary education (Primary 1 to Primary 6), three years of junior secondary education (Secondary 1 to Secondary 3), and three years of senior secondary education (Secondary 4 to Secondary 6) for all children attending public sector schools. All public sector local schools deliver the curriculum proposed by the HKSARG. In both primary and secondary education, students are

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required to study Chinese language, English language, Mathematics, Physical Education, Science, Arts and Technology education. In secondary education, senior secondary education students study Liberal Studies as a core subject in additional to Chinese language, English language and Mathematics. They also choose two to three electives from Science, Arts and Technology streams.

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TE and ELE are two out of eight key learning areasii in different stages of schooling in Hong Kong public sector schools. According to the TEKLA Curriculum Guide, technology is defined as “the purposeful application of knowledge, skills, and values and attitudes in using resources to create products, services or systems to meet human needs and wants”. (Curriculum Development Council, 2017: 3). TE requires students to understand and be aware of different types of widely used information and communication technology (e.g., computer programs, smartphones, facebook, the

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Internet), computer-related skills (e.g., word processing) and materials, know how to operate tools and equipment for learning and communication (e.g., search and retrieve information from the Internet), and finally manage technology in living environments. The aims of TE, as stipulated in the Curriculum Guide, are to “develop technological literacy in students through the cultivation of

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technological capability, technological understanding and technological awareness” (p. iv) so as to enable them to collaborate and interact with people, and learn by themselves through technology. The Curriculum Guide also calls for integration with other seven key subject areas (p.15). To implement the intended goals and encourage IT integration into the school curriculum, the Education Bureau of Hong Kong organizes regular IT in education seminars for school teachers of various subjects. From 2011-2014, an e-learning pilot schemeiii that encouraged IT application and integration with subjects was launched. The scheme successfully involved 33 primary and 18 secondary schoolsiv and new strategies such as enhancing IT infrastructure was reported in an education policy document in 2015v. English language, which is a key learning area, focuses on providing students with a wide range of learning experiences to enhance the ability, knowledge and generic skills to use English for personal and life purposes. English language education (ELE) is an authentic context in which technology knowledge, skills and values can be applied. Nevertheless, English language is generally taught in a traditional pen-and-paper setting, though teachers will ask students to search for information via the Internet or do drill-and-practice work and instructional exercises (Education Bureau, 2015).

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In the same ways as many public sector schools, the participating schools follow both TE and ELE KLA Curriculum Guides. TE are taught through the subject of Computer Literacy, Design and Technology, and the four English language skills are usually taught in the classroom in addition to grammar and vocabulary teaching. While the TEKLA Curriculum Guide encourages public sector

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schools to leverage technology to facilitate collaboration and interaction, the three participating schools seldom require their students to work on e-English language assignments. Instead, teachers sometimes ask students to download or read online materials at home as preparatory work for class activities, as observed in the students’ works and noted by the Chairpersons of the English Panel when a computer-assisted English writing project was introduced to the schools. Students are not allowed to use any mobile devices to complete work or communicate with each other during school day. In case any computer-assisted language learning activities are initiated by teachers, they are conducted in the school’s computer laboratory. In sum, irrespective of school type, English

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language learning activities are expected to involve information and communication technology applications and vice versa although such applications may be in various forms, subject to teachers’ pedagogical preference. 5.1.2 Penetration and Usage rate of Information and Communication Technology at the Personal and Societal Levels in Hong Kong

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Aside from TE at school, information and communication technology has been widely used at the personal and societal level in Hong Kong. According to a recent report on the usage of Information Technology and the Internet by Hong Kong residents from 2000 to 2016 (Census and Statistics Department, 2018), the number of households with personal computers at home and their usage of

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computers, the Internet and smartphones drastically increased over the past 16 years. In 2016, 79.5% of households had personal computers at home connected to the Internet or smartphones. Approximately 99% and 88% of 10-24 year-old adolescents had personal computers and smartphones during the 12 months before enumeration respectively. The penetration rate of personal computer was higher than that of older persons. In addition, around 99% of Hong Kong citizens used the Internet. Residents aged 10 and above used the Internet mainly for communication or interaction (97%), followed by information search (90.8), entertainment (88%), school or personal affairs (38.8%) and shopping or finance transaction (37.8%). About 20% of the residents aged 10 and above used the Internet for 50 hours or above per week. The penetration and usage rate of information technology by residents in Hong Kong in different age groups is reasonably high. Given the fact that almost every adolescent in Hong Kong has a personal computer, and no student reported non-access to information technology, socioeconomic status (SES) did not seem to be a barrier to the sampled students’ access to technology. 5.2 Method of study 5.2.1 Participant profiles

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The study was conducted in one girls’ and two co-educational government aided secondary schools in Hong Kong which participated in a computer-assisted English writing project consisting of five writing workshops designed for Secondary 3 (S3) and Secondary 4 (S4) students aged 13 to 16. With a view to better understanding adolescent learners’ attitudes, perceptions and self-efficacy in

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using technology before the commencement of the project, a questionnaire survey was administered to the two levels of students, representing lower intermediate to intermediate level of English proficiency in general, at the three schools. The questionnaire was anonymous and the students participated on a voluntary basis. All questions were written in both standard written Chinese and English (see Appendix). Considering that this might be the students’ first experience in answering a questionnaire, the students were allowed to complete it at home. As they were minors during the research period, parental consent was sought as well. The completed questionnaires were returned together with the signed parent consent form to a collection box outside the school’s General Office

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in a week’s time. (Non)participation in the survey would not affect their enrolment in the writing workshops. The data collection procedures complied with the university’s research ethics policy. With the assistance of a teacher who acted as a liaison person for the project in each school, 250 questionnaires were distributed to the two target groups of students who had expressed an interest in joining the writing workshops to be conducted in the three schools. Finally, a total of 193 (77.2% return rate) students (80% females, 20% males) aged between 13.5 and 16.9 years (M=14.46,

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SD=2.55) completed the survey. Since one of the participating schools is a girls’ school and more female students registered for the writing workshops than male students in the other two schools in response to the call for participation, gender imbalance constitutes a limitation in the interpretation of findings.

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Among the 193 respondents, 96.4% speak Cantonese at home, while 3.6% speak other languages or dialects (English, Putonghua, Hakka, Fukien and Chiuchowvi) at home. About half of them (55.9%) started using technology at primary one (at approximately 6 years old) for various purposes in addition to technology education stipulated in the curriculum prepared by the HKSARG for all public sector schools. Regarding the types of technology frequently used for English language learning (Q.5 of Section 1), smartphones were found to be most frequently used (3.31 on a 5-point scale), followed by computers (2.94) and tablets (2.23). Thus, the respondents had spent more hours on smartphones than on computers, and least on tablets over the past six months. To input English, they most frequently used the keyboard, followed secondly by touchscreen and then voice. 5.2.2 Measures The survey consisted of items that examined the factors of attitude, perceived usefulness, and self-efficacy with items adapted from those in the study of Lai et al. (2012). As the present study was about the domain of English language learning in the Hong Kong secondary school context, some of the items were slightly modified (see Appendix). To unravel the relations between

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perceived usefulness of technology and actual adoption of technology specifically for school-related English learning activities, questions were specially designed to measure four constructs: perceived usefulness for learning English (individualized; collaborative learning purposes) and actual application of technology for learning English (individualized; collaborative learning purposes) in

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the school context. Taking into account the definition of technology in the TEKLA Curriculum Guide (2017c) and the survey on information technology administered by Census and Statistics Department (2018), technology, in this study, referred to a list of commonly used information and communication technology tools (e.g., computers, the Internet, smartphones, school discussion forums) and related skills (e.g., searching the Internet) for English language learning, particularly for school-related tasks. As Appendix shows all the scales and respective items used in the study, they are not detailed

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further here. The following is a summary of the items for each construct.

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Attitude towards the value of technology. Three items were developed. These asked the students about their thoughts and beliefs regarding the value of technology for English language learning. Respondents were asked to rate their level of agreement on a 5-point Likert scale, from 1 (strongly disagree) to 5 (strongly agree).

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Perceived usefulness of technology. Three items were developed. Some original items from Lai et al.’s questionnaires (2012) were modified to fit in with the study and the respondents’ learning environment. Words such as ‘the discipline’ and ‘in the university’ in Lai et al.’s questionnaire were changed to ‘English language learning’. Respondents were asked to rate their level of agreement on a 5-point Likert scale, from 1 (strongly disagree) to 5 (strongly agree).

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Self-efficacy. Three items concerning the respondents’ familiarity with a number of basic technology skills were developed. Those skills included spell check, grammar check, and retrieving information. The respondents were asked to indicate their level of familiarity with these skills by rating them on a 5-point Likert scale, from 1 (don’t know) to 5 (familiar). Perceived usefulness of technology for English language learning in the school context. Six items were developed (Appendix 1). As English language learning involves learner engagement in both individualized and collaborative learning (interaction with other learners) activities when using computer technology, three of the six items addressed the purposes of increasing learning opportunities, sustaining learning, and expanding English language knowledge (all classed as personal outcomes). The other three items addressed application for collaborative learning such as sharing, seeking help, and consulting teachers. These collaborative learning activities comprise an important generic skill and objective to achieve in the English Language Education Key Area Curriculum Guide and Secondary Education Curriculum Guide (2017a; 2017b). The respondents

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were asked to rate the extent of usefulness for each item on a 5-point Likert scale, from 1 (least useful) to 5 (most useful). They were asked to respond to the items as they understood them.

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Application of technology for school-related English language learning activities. Parallel to the six items for perceived usefulness for English language learning, another six items asked the students about their frequency of actual technology usage for individualized and interpersonal school-related English learning activities. They responded from 1 (never) to 5 (frequently—more than 7 hours) per week, as they understood the terms. Altogether there were 21 items used for the scales. In addition to these items, the bilingual questionnaire also elicited some background information of the respondents, including age, gender, first language (L1), the first time they used technology, and some prior technology use experience.

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5.2.3 Establishing the measures

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The measures were established in two stages: (1) performing confirmatory factor analysis (CFA), and (2) checking Cronbach’s alpha reliability for each scale.

For the CFA (see Kline, 2005; Jöreskog & Sörbom, 2005), a variety of goodness-of-fit statistics (i.e., Tucker-Lewis Index, TLI: Tucker & Lewis, 1973; the Comparative Fit Index, CFI: Bentler, 1990;

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and the root mean square error of approximation, RMSEA: Browne & Cudeck, 1993) were used as primary indices for model evaluation. Values of TLI and CFI greater than .90 were considered acceptable (Hu & Bentler, 1999); and the value of RMSEA below .08 was taken as indicative of a reasonable fit (Browne & Cudeck, 1993). Other criteria for an acceptable model were:

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(1) Cronbach’s alpha coefficient being > .60 (Cronback, 1951); (2) factor loadings for items on their corresponding scale being > .30; and (3) factor correlations being < .90 for each construct to be distinct from others.

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Three CFA models were tested. Table 1 shows the goodness of fit of each model. Model 1, testing the seven factors (attitude, usefulness, self-efficacy, usefulness for individualized English learning, usefulness for collaborative English learning, application for individualized English learning, and application for collaborative English learning), resulted in a reasonable fit: χ² (176) = 268.42, TLI = .902, CFI = .925, RMSEA = .052. However, an inspection of the latent correlations found that the correlation between the domain-specific usefulness factors (i.e., usefulness for individualized English learning and usefulness for interpersonal English learning) = .91, which means that these two usefulness factors are not separable as two different constructs. Model 2 combined these two factors as one. This seven-factor model (Model 1) also resulted in a reasonable fit: χ² (183) = 268.42, TLI = .905, CFI = .925, RMSEA = .052. To examine whether the application factors should be treated as two factors like the usefulness factors in Model 1 or as a single factor as in Model 2, Model 3 was a six-factor model treating both individualized and collaborative applications as a

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single construct. Model 3 did not result in a reasonable fit, with TLI <.90: χ² (189) = 300.58, TLI = .890, CFI = .910, RMSEA = .056 (Table 1). Considering all three CFA models, Model 2 was accepted as the best solution, which is presented in Table 2. The means and standard deviations are also presented. As can be seen in Table 2, all the factor loadings are acceptable (>.30) and all latent correlations support discriminant validity (<.90). Table 2 presents the reliability estimates of the scales. All alpha values were above .60 (i.e., .63 for

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attitude, .79 for general usefulness, .60 for efficacy, .79 for usefulness for learning English in the school context, .75 for individualized and .64 for collaborative applications). The means for attitude, general usefulness, efficacy, and usefulness for learning English in the school context were all above the mid-point of 3 on a 5-point scale (Ms from 3.40 to 3.50; Table 2). However, the actual

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individualized and collaborative applications were both below the mid-point (Ms = 2.51 and 2.24, respectively).

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5.2.4 Analysis

To answer RQ1, the correlations between the learner factors and applications of technology were examined. To answer RQ2, the correlations between the participants’ applications of technology for individualized and collaborative English learning as well as age differences were examined. To

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answer RQ3, repeated-measures analysis of variance (ANOVA) was conducted to examine any discrepancy between the participants’ perceived usefulness of technology and their actual application of technology. Finally, a regression analysis using a structural equation approach was conducted to examine which of the generic variables (attitude, usefulness, and efficacy) are

6. Results

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relatively stronger as correlates of the English-specific variables (perceived usefulness and application).

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Learner factors and correlates of individualized and collaborative learning applications of technology for English language learning in the school context In response to RQ1, all four learner factors (attitude, general usefulness, self-efficacy, and usefulness for English language learning) were found to be significant correlates (r ranging from .13 to .49; Table 2) for both individualized and collaborative learning applications. However, the individualized and collaborative learning factors displayed different patterns of correlation with each of these learner factors (while an r of .49 was moderate, some correlations were much weaker, at about .10). Attitude was more highly correlated with individualized learning (r=.49) than with collaborative learning (r=.23). In contrast, self-efficacy showed a low correlation with collaborative learning (r=.20) and individualized learning (r=.13), both correlations being weak, according to Cohen (1992). These seem to imply that individualized English language learning is more related to attitude. Although not as high as attitude, which was the highest correlate of individualized learning

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(r=.49), for both types of application, perceived usefulness in general terms (r=.41 and .42 respectively) and usefulness specifically for English language learning (r=.38 and .34 respectively) seemed also to have an important role to play in promoting technology adoption for English language learning in the school context. Interestingly, even though the students did not perceive general and specific usefulness as the same thing (r=.49), both usefulness constructs did seem to play quite a similar part in learners’ technology adoption.

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Differentiation between the application of technology for individualized and collaborative English language learning in the school context, and relations with age

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As for RQ2, since Model 2 supports the differentiation of actual individualized and collaborative learning applications but not a separation of perceived usefulness for such application for English language learning at schools (Table 1), evidence shows that while adolescent learners tended to perceive usefulness as common across both individualized and collaborative learning processes,

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their actual learning behaviors could be quite distinct (see factor correlations of Model 2 in Table 2). Further evidence was observed in the correlation between individualized and collaborative learning (r=.77), which indicated that they were clearly distinct from each other (much lower than the high correlation for the parallel constructs of usefulness at above .90). However, the correlation of .77 also indicated that individualized and collaborative applications cannot be taken as dichotomous or exclusive of each other. In essence, the boundary between the two types of activities can be blurry at times. In Table 2, age was found to have almost no correlation with either application (r=-.01

Table 1 here Table 2 here

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with individualized and r=-.09 with collaborative). In fact, age was not significantly correlated with attitude (r=-.15) and general usefulness of technology (r=-.10), indicating that students of different ages held quite similar perceptions. The only statistically significant correlation was with self-efficacy (r=-.36), indicating that younger students tended to perceive higher self-efficacy.

Perceived usefulness of technology versus actual adoption of technology for individualized and collaborative school-related English language learning activities In response to RQ3, the series of repeated-measures ANOVA shows large discrepancies between the adolescents’ perceptions of the usefulness of technology for English language learning and their actual use of such technology for this purpose for school-related tasks. As shown in Table 3, for the individualized application (increase, sustain, expand), each of the perceived usefulness measures was well above 3 while actual application was well below 3 on a 5-point scale (difference ranging from 33% to 47%). Similar patterns were observed for the collaborative learning activities (share, seek, consult), and the differences between perceived usefulness and actual application were all statistically significant at p<.001 (ranging from 38% to 61%). As shown in Table 3, perceived

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usefulness was higher than actual application for the variables of Increase (F (1, 129)=187.10, MSE=0.46), Sustain (F (1, 129)=212.31, MSE=0.50), Expand (F (1, 129)=148.81, MSE=0.51), Share (F (1, 129)=185.18, MSE=0.60), Seek (F (1, 129)=160.78, MSE=0.48), and Consult (F (1, 129)=280.38, MSE=0.61), all p-values <.001, and all η2 values >.40. Specifically, for the variable of

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Increase, actual application (M=2.61) was lower than perceived usefulness (M=2.61). The same pattern was observed for Sustain (Ms of 2.24 vs. 3.29), Expand (Ms of 2.68 vs. 3.57), Share (Ms of 2.16 vs. 3.23), Seek (Ms of 2.37 vs. 3.26), and Consult (Ms of 2.17 vs. 3.50). Hence consistent across various applications, perceived usefulness and actual application behavior were not

Table 3 here

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commensurate with each other. This casts doubt on researchers’ claims of any effectiveness in enhancing learners’ perceptions of the usefulness of technology for the purpose of promoting actual behavioral application of it for specific educational aims.

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Although not central to our RQs, a potential hypothetical question that may be further explored is which of the generic variables (attitude, usefulness, efficacy) may be more strongly related to the English-specific variables (perceived usefulness and actual applications for English learning), controlling for age and gender. A path model was tested. Figure 1 shows the model, which had a marginal fit, χ² (198) = 312.12, TLI = .896, CFI = .910, RMSEA = .058. As can be seen in Figure 1, the relative associations of variables with perceived usefulness for English learning and actual individualized and collaborative applications for school-related activities vary. Attitude was found

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to be significantly associated with individualized application (β=.39), but not with the other two English learning variables. Perceived usefulness of technology in general was significantly associated with perceived usefulness for English learning (β=.35), which was logical and not surprising, but it was significantly associated with collaborative application (β=.42), not

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individualized application. Self-efficacy was significantly associated with perceived usefulness for English learning (β=.43), but not with actual applications. Age effects were found only for perceived usefulness for English learning (β=.25), indicating that older students tended to favor more technology as a useful tool for English learning. Whereas gender effects were all negligible, interpretations of any gender effect should be made with caution anyway, due to the imbalance of gender in this sample. While this supplemental analysis suggests a new direction for further investigations, the patterns found here were based on a hypothetical model, which can however be more appropriately tested with longitudinal data. Figure 1 here 7. Discussion Largely in line with the positive attitudes and perceptions of learners towards technology among tertiary students reported in the literature (Cai et al., 2017; Cheung & Vogel, 2013; Cilek & Yesilyurt, 2013; Lee & Lehto, 2013), the adolescent English learners in Hong Kong generally feel that

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technology has the potential to help them learn English, think and be creative. They are willing to use and make an effort to use it for different purposes. While positive attitude (Ajzen, 1985, 1991, 2005), self-efficacy (Bandura, 1982; 1997) and perceived usefulness (Davis, 1989) have been found to be powerful predictors of technology use (Cheung & Vogel, 2013; Cilek & Yesilyurt, 2013; Lee &

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Lehto, 2013; Liu, Chen, Sun, Wible & Kuo, 2010; Saadé & Bahli, 2005; Shank & Cotton; 2014), the present study provides additional evidence to demonstrate the patterns of actual technology adoption by the Chinese adolescent learners of English. The analysis shows that the adolescent learners’ attitude is more related to individualized learning purposes (r=.49; β=.39) than to collaborative learning purposes (r=.23). In other words, the adolescent learners with a positive attitude are more likely to use technology to achieve their English learning purposes, but most likely for personal gains than for interactions through sharing or discussing a project with peers and teachers online. It seems that this personal factor not only predicts technology use as reported in the literature, but may

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be related more specifically to a practical purpose of technology use (Tsai & Tsai, 2010: Clark et a., 2009). Moreover, the adolescent learners’ perceived usefulness of technology in general terms (r=.41 and .42, respectively) and specifically for English language learning (r=.38 and .34, respectively) seem to contribute to technology adoption for collaborative school-related learning activities only (β=.42). This lends further support to Clark et al.’s study (2009) on Davis’s claims about the relationship between perceived value of technology and use (1989). Although personal computers and the Internet use are popular among adolescents in Hong Kong as shown in a report on the usage of

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information technology and the Internet over the past ten years (Census and Statistics Department (2018) (see 5.2.2), it is still not clear from the document how - and in what ways - adolescent learners use technology. The analysis of the present study, however, indicates a discrepancy between the popularity of technology use by a group of Chinese adolescent English learners and their limited

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technology use for interpersonal language learning purposes. Perhaps limited use of technology for individualized learning may be caused by the adolescent learners’ school context in which English language is learnt. There are online activities but are for specific purposes, and interactions with peers and teachers for school work are not frequently required (see 5.1). Asking learners to work on online English group projects through technologies does not seem to be a common practice in the three participating schools, as observed from their records. Therefore, both parties may not perceive any school-related language learning activities that involves exchange or sharing of ideas through technology to be of the same significant value as individualized language learning activities. Likewise, teacher-student online interactions may not be perceived to be as effective as face-to-face interactions, though the curriculum encourages the integration of English learning and Information Technology (Curriculum Development Council, 2017a; b). This is consistent with the findings of Pena and Yeung (2009), that university students prefer face-to-face interactions in language learning. Similarly, technology application for individualized learning in the three schools might be more successful with the older adolescent learners than their young counterparts as age effects were found only for perceived usefulness for English learning (β=.25).

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variation in their perception towards the value of technology and actual technology adoption for school-related English learning activities. The minimal gender gap, as implied from the present study on English learning, lend some support to the gradual reduction of two genders reported in the literature regarding gender differences in technology use in general (Cai, Fan & Du, 2017), including in the Asian context (Tsai & Lin, 2004; Tsai & Tsai, 2010; Zhou, 2014). 8. Implications

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If the adolescent learners’ attitudes and perceptions of the value of technology is positive and their self-efficacy is high, it is likely that they will be more willing to apply technology for both individualized and collaborative language learning purposes. Promoting application of technology for both types of learning activities, particularly the latter, English teachers in Hong Kong, particularly the three participating schools, could further adopt creative online collaborative tasks in addition to individual learning exercises in and out of the school contexts. Technology-supported or computer-assisted collaborative tasks for English learning have been advocated in the literature

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(Thomas & Reinder, 2010) and have been practiced at all levels of schooling in different cultural contexts, including Hong Kong. For instance, iPads and other digital devices such as Google docs are adopted for primary New Zealand students (Falloon, 2015). A Web-based Essay Critiquing System (Lee et al., 2013), wikis (Mak & Coniam, 2008), and mobile devices (Hwang, Chen, Shadier, Huang,

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& Chen, 2014) have been developed and used for some Hong Kong secondary students and elementary Taiwan students to practice writing respectively. WhatsApp groups between secondary school Israeli teachers and students are formed for communication and support (Bouhnik & Deshen, 2014). Nowadays, many schools and teachers have started to design various technology-based tasks to promote individualized, collaborative activities with peer or teacher support whereby students see the value of technology use. However, the use of computer-based pedagogical activities is not solely influenced by learners’ self-efficacy, positive attitude and perception towards computer-assisted language learning, but also teachers’ (Celik & Yesilyurt, 2013; Hubbard, 2011; Vekiri, 2010a). Technology is a tempting alternative to promote language learning and bring added value to traditional classroom teaching; nonetheless, teachers should know how to make good use of tasks or integrate them into the courses for effective teaching (Kirkwood & Price, 2005). Teachers do not need to follow the vogue or simply comply with official requirements or instructions (Yeung, Lim, Tay, Lam-Chiang & Hui, 2012; Yeung, Taylor, Hui, Lam-Chiang & Low, 2012). Instead, they should understand the learning goals for students, the language, culture and instructional resources they have, and the combination of resources and assessment methods to evaluate the effective use of the resources (Chun, Smith, & Kern, 2016).

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9. Limitations of the Study

The study has six limitations. First, there is a lack of gender balance in the data. The imbalance was primarily caused by the number and types of schools - one girls’ and two co-educational schools -

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which participated in the computer-assisted English writing project conducted by the first researcher. As these schools expressed an interest in the project and the completion of the questionnaire was heavily based on the students’ willingness and more female students tended to attend English language programs, particularly about writing, the risk of having gender imbalance could exist. As the writing project targeted at Secondary 3 and 4 students around 13-16 years old in only three schools, the second and the third limitations are the adolescent learners’ age group, and the small sample size of the survey. In addition, the students’ self-reported perceptions and attitude towards technology, particularly the Internet and computer usage, as well as the frequency of use could be

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impacted by their parents’ support (Vekiri & Chronaki, 2008), parental parenting style and socio-economic status (Chou, Chou, & Chen, 2016; Hatlevik et al., 2018; Lau & Yuen, 2016), teachers’ pedagogical practices (Vekiri, 2013), previous ICT experience (Rohatgi, Scherer & Hatlevik), particularly technology education in Hong Kong schools since primary education (Curriculum Development Guide 2017c). Although the three schools have followed the official TEKLA Curriculum Guides and have offered

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relevant subjects for TE, technology does not seem to be widely adopted in their English language education. More attention and effort from the school authority could be paid to the integration of technology and English language education in response to the regular IT seminars and IT-related projects initiated by the government (Refer to 5.1.1). Finally, while the questions were written in both standard written Chinese and English, the students might associate technology:科技 with

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different technology tools, subject to their interpretation of the type of technology with which they are familiar. The findings, in this light, may not be generalizable to other learning environments, and should be treated with caution. Further research on other age groups of adolescents (e.g., 11-12 or 17-19 year olds) with different academic performing levels (Zhou, 2014), socio-economic status and parenting styles, and regions will enhance our understanding of the topic. Questions of the survey could be more focused by referring to one or two technology tools to avoid any variation in interpretation. Longitudinal data on the patterns of personal factors and actual adoption of technology use for English learning could be collected to test the hypothetical model more thoroughly. Finally, as the present data did not differentiate students’ use of technology in class or out of class which may trigger different behaviors (Vekiri, 2010b), further investigations may help delineate contextual differences. 10. Conclusion In essence, the present study has shed new light on the relations between adolescent language learners’ self-efficacy, attitude towards technology, perceived usefulness of technology, and actual

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technology application. While it is true that adolescent learners’ beliefs, positive attitudes and perceptions do relate to technology use, these personal factors correlate with technology application for individualized and collaborative learning purposes in different ways. More importantly, there are discrepancies between perceived usefulness of technology and actual adoption of technology.

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All these show that the functions of the three learner factors are not only associated with learners’ willingness to use technology as reported in the literature but also actual adoption of technology for learning. The discrepancies should be taken into account by teachers when they choose to use technology to support English language learning and education, particularly in the Hong Kong learning environment. This is because English learners’ perception toward technology use can be influenced by the ways in which English is taught and technology is used by teachers in schools. In light of its limitations, further investigations could include other age groups and social factors in analyzing the relationship between perception and actual adoption of technology use. Furthermore,

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as the adolescent language learners in this study are from the same or very similar socio-cultural and educational contexts, generalizations of the findings may be restricted. However, applying the same survey questionnaires to other institutional and cultural contexts or age groups on a larger scale, and comparing the analysis with that of the current study, could shed further light on the topic under investigation. Finally, the study has contributed to the literature by pointing to the potential of acquiring a better understanding of not simply what students, particularly Chinese adolescent learners of English, feel or think about technology, but also what they actually do with technology

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for different learning purposes.

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Appendix Scales and related items

Attitude (agree) (From 1 = strongly disagree to 5 = strongly agree) 1. Technology has become extremely important in enhancing my knowledge about English. 科技對我提昇英文知識極其重要

科技對於擴闊我的英語學習機會變得重要

3. I believe in the potential of technology for learning English. 我相信科技有潛力用於英語學

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2. Technology has become important for me in expanding my English learning opportunities.

科技可以幫我開發新的思維方式

3.

Technology can help me think critically.

科技可以幫我的批判性思考

Technology can help me be more creative.

科技可以幫助我變得更有創意

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2.

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Usefulness (agree) (From 1 = strongly disagree to 5 = strongly agree) 1. Technology can help me develop new ways of thinking.

Efficacy (how familiar with the use of technology-related skills) (From don’t know, know but haven’t tried, have tried to familiar) 1. Spell check on word processing applications. 文書處理軟件的英文拼寫檢查功能

2. Grammar check on word processing applications 文書處理軟件的英文文法檢查功能

3. Retrieving information with search engines (e.g. Google advanced search).

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使用搜索引擎獲取資訊（如谷歌進階搜尋）

Perceived Usefulness: Individualized application (how useful) (From 1 = least useful to 5 = very useful) 1. Increasing my English language learning opportunities.

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科技對於擴闊我的英語學習機會變得重要

2. Sustaining/enhancing my motivation in English language learning 科技對於持續或/及提昇我對英語學習的推動力變得重要

3.

Expanding my knowledge of the English language

科技對我提昇英文知識極其重要1 5

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3.

Perceived Usefulness: Collaborative application (how useful) (From 1 = least useful to 5 = very useful) 1. Sharing my views/work related to my studies contents online (e.g. Facebook, WhatsApp groups) 2.

透過網絡（如Facebook或WhatsApp群組）分享關放學習內容的想法或作品1 5 Q8.9.2 1 5

2. Seeking help or support from classmates/friends online (e.g. WhatsApp messages, forum posting) 透過網絡向同學或朋友尋求幫助與支援（如透過 WhatsApp 訊息或在討論區發文）

3. Consulting teachers online 透過網絡諮詢老師

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Actual: Individualized application (how often) (From never, rarely = < 1 hour, sometimes = 1-3 hours, often = 4-7 hours to frequently = > 7 hours) 1. Increasing my English language learning opportunities 科技對於擴闊我的英語學習機會變得重要

2. Sustaining/enhancing my motivation in English language learning 科技對於持續或/及提昇我對英語學習的推動力變得重要

3. Expanding my knowledge of the English language 科技對我提昇英文知識極其重要 1 5

2.

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Actual: Collaborative application (how often) (From never, rarely =< 1 hour, sometimes = 1-3 hours, often = 4-7 hours to frequently = > 7 hours) 1. Sharing my views/work related to my studies contents online (e.g. Facebook, WhatsApp groups) 透過網絡（如Facebook或WhatsApp群組）分享關放學習內容的想法或作品1 5 Q8.9.2 1 5

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3. Seeking help or support from classmates/friends online (e.g. WhatsApp messages, forum posting)

透過網絡諮詢老師

i

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透過網絡向同學或朋友尋求幫助與支援（如透過 WhatsApp 訊息或在討論區發文）

4. Consulting teachers online

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There are three types of schools in Hong Kong. They are: government schools fully operated by the government; aided schools sub-vented by the government and run by voluntary bodies; and private schools (Hong Kong: The Facts – Education at https://www.gov.hk/en/about/abouthk/factsheets/docs/education.pdf, retrieved on 24 March 2018). ii There are eight key learning areas in junior secondary education in Hong Kong. They are: Chinese language, English language, Mathematics, Science, Personal, Social and Humanities, Technology, Arts and Physical education. There are only six key learning areas in senior secondary education, namely Chinese language, English language, Mathematics, Liberal Studies, two or three elective subjects and other learning experiences. (Education Bureau at http://www.edb.gov.hk/en/curriculum-development/cs-curriculum-doc-report/8-key-area/index.html.) iii Information about the pilot scheme is available at http://edbsdited.fwg.hk/e-Learning/eng/index.php?id=2. iv The information is extracted from the Executive Summary of the Report on the Research Study on the Pilot Scheme on E-learning in Schools, published by the Education Bureau in 2015. v For details, please refer to the document titled “The Fourth Strategy on IT in Education”, accessible at https://www.edb.gov.hk/en/edu-system/primary-secondary/applicable-to-primary-secondary/it-in-edu/ite4.html. vi According to the Thematic Household Survey Report No.52 on the Use of Language in Hong Kong prepared by the Census and Statistics Department (2016), 90.3% people aged 6 – 65 in Hong Kong reported that they speak Cantonese as their mother tongue, 3.2% speak Putonghua and 3.0% speak other Chinese dialects. Chiuchow, Hakka and Fukien are other Chinese dialects. About 1.4% people use English as their mother tongue.

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Acknowledgements

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This work was supported by the Standing Committee on Language and Education Research (SCOLAR), Hong Kong Education Bureau, numbered LF3-EL/164/14 .

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Table 1: Goodness of Fit of Models 2

χ

Model

df TLI CFI RMSEA

8 factors – 2 English useful & 2 apply factors 268.42 176 .902 .925 .052

Model 2

7 factors – 1 English useful & 2 apply factors 276.50 183 .905 .925 .052

Model 3

6 factors – 1 English useful & 1 apply factors 300.58 189 .890 .910 .056

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Model 1

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Table 2. Solution of Model 2

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Attitude Useful Efficacy English Use Individualized Collaborative Age Uniqueness Alpha .63 .79 .60 .79 .75 .64 Mean 3.50 3.45 3.41 3.40 2.51 2.24 (SD) (0.68) (0.83) (0.55) (0.59) (0.77) (0.73) Min 1.00 1.00 1.33 1.17 1.00 1.00 Max 5.00 5.00 4.33 5.00 5.00 5.00 Factor Loadings Enhance .48* .77* Opportunity .63* .61* Potential .68* .53* Thinking .81* .34* Critical .87* .25* Creative .61* .63* Spell .60* .64* Grammar .71* .50* Retrieve .47* .78* Useful Increase .64* .59* Useful Sustain .67* .55* Useful Expand .59* .66* Useful Share .66* .57* Useful Seek .57* .68* Useful Consult .60* .64* Apply Increase .68* .54* Apply Sustain .75* .44* Apply Expand .66* .56* Apply Share .71* .50* Apply Seek .61* .63* Apply Consult .53* .72* Age 1 0 Factor Correlations Attitude -Usefulness .58* -Efficacy .03 .46* -English Use .26* .51* .49* -Personal .49* .41* .13* .38* -Interactive .23* .42* .20* .34* .77* -Age .15 -.10 -.36* .06 -.01 -.09 --

Note: * p<.05. N=193. English Use = perceived usefulness of technology for English learning. Individualized = individualized application. Collaborative = collaborative application.

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Table 3. Perceived Usefulness of Technology for English learning vs. Actual Application MSE

η2

2.61 (0.97)

187.10**

0.46

.49

3.29 (0.79)

2.24 (0.98)

212.31**

(0.79) 0.50

.53

Expand

3.57 (0.78)

2.68 (0.88)

148.81**

(0.79) 0.51

.44

Share

3.23 (0.89)

2.16 (0.90)

185.18**

(0.92) 0.60

.49

Seek

3.26 (0.94)

2.37 (0.95)

160.78**

(1.06) 0.48

.46

Consult

3.50 (0.81)

2.17 (1.00)

280.38**

(1.19) 0.61

.59

(10.84)

(11.08)

M (SD)

M (SD)

Increase

3.55 (0.82)

Sustain

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Note: ** p<.001. N=193.

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F(1, 129)

Variables

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Age

-.04

English Useful

-.11 .01

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Attitude

SC

-.14

Actual Individualized

.39*

.35*

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.19

-.05

-.05

Useful

RI PT

.25*

EP

-.00

.42*

Actual Collaborative

.43*

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.04

Efficacy

Figure 1. Path model. *p<.05

-.00

ACCEPTED MANUSCRIPT Highlights 1. A discrepancy between application and perceived usefulness of technology 2. Attitude was related to use of computer technology for individualized learning purposes 3. Self-efficacy was related to perceived usefulness of technology for English

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learning