Mobile apps for science learning: Review of research

Computers & Education 94 (2016) 1e17

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

Mobile apps for science learning: Review of research Janet Mannheimer Zydney*, Zachary Warner School of Education, University of Cincinnati, Cincinnati, OH 45221, United States

a r t i c l e i n f o

a b s t r a c t

Article history: Received 5 November 2014 Received in revised form 29 October 2015 Accepted 2 November 2015 Available online 5 November 2015

This review examined articles on mobile apps for science learning published from 2007 to 2014. A qualitative content analysis was used to investigate the science mobile app research for its mobile app design, underlying theoretical foundations, and students' measured outcomes. This review found that mobile apps for science learning offered a number of similar design features, including technology-based scaffolding, location-aware functionality, visual/audio representations, digital knowledge-construction tools, digital knowledge-sharing mechanisms, and differentiated roles. Many of the studies cited a specific theoretical foundation, predominantly situated learning theory, and applied this to the design of the mobile learning environment. The most common measured outcome was students' basic scientific knowledge or conceptual understanding. A number of recommendations came out of this review. Future studies need to make use of newer, available technologies; isolate the testing of specific app features; and develop additional strategies around using mobile apps for collaboration. Researchers need to make more explicit connections between the instructional principles and the design features of their mobile learning environment in order to better integrate theory with practice. In addition, this review noted that stronger alignment is needed between the underlying theories and measured outcomes, and more studies are needed to assess students' higher-level cognitive outcomes, cognitive load, and skill-based outcomes such as problem solving. Finally, more research is needed on how science mobile apps can be used with more varied science topics and diverse audiences. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Applications in science education Mobile learning Interactive learning environments Cooperative/collaborative learning

1. Introduction Mobile devices are becoming increasingly popular and connected with our daily lives. Each new version of these devices brings innovative features that make them more convenient and affordable, and new apps continually become available that make our lives easier. These advances have prompted educators and researchers to utilize these devices to promote teaching and learning. There is great potential in using mobile devices to transform how we learn by changing the traditional classroom to one that is more interactive and engaging (Shen, Wang, & Pan, 2008). It allows educators to teach without being restricted by time and place, enabling learning to continue after class is over or outside the classroom in places where learning occurs naturally (Huang, Lin, & Cheng, 2010). It also gives educators the ability to connect with learners on a more personal level with devices that they use on a regular basis (Ward, Finley, Keil, & Clay, 2013). Finally, sensing technologies enable learning to be personalized and customized to the individual learner (Chu, Hwang, Tsai, & Tseng, 2010). Given the prevalence of mobile devices in education, research on mobile learning is rapidly increasing (Hung & Zhang, 2012; Hwang & Tsai, 2011; Wu et al., 2012) and thus has been reviewed in several studies (Cheung & Hew, 2009; Hung &

* Corresponding author. E-mail address: [email protected] (J.M. Zydney). http://dx.doi.org/10.1016/j.compedu.2015.11.001 0360-1315/© 2015 Elsevier Ltd. All rights reserved.

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Zhang, 2012; Hwang & Tsai, 2011; Hwang & Wu, 2014; Wu et al., 2012). Some reviews focused on specific aspects of mobile learning, such as mobile learning games (Avouris & Yiannoutsou, 2012; Schmitz, Klemke, & Specht, 2012), mobile computersupported collaborative learning (Hsu & Ching, 2013), or mobile apps (Jeng, Wu, Huang, Tan, & Yang, 2010). Trends in the literature have also been reported across multiple reviews. For example, reviews have shown that mobile learning is highly motivating for students (Hsu & Ching, 2013; Hwang & Wu, 2014; Schmitz et al., 2012). On the other hand, some of the findings from these past reviews have been contradictory. For example, reviews reported mixed findings on the effect of mobile environments on learning outcomes. Hwang and Wu (2014) did a review on mobile learning studies spanning 2008e2012 from select journals and found that 83% of the studies that measured learning achievements reported positive outcomes. Similarly, Hsu and Ching (2013) reviewed studies on mobile computer-supported collaborative learning from 2004 to 2011 and reported that six of the nine studies found positive improvements in students' understanding and application of concepts. In contrast to these positive findings, Schmitz et al. (2012) reviewed studies on mobile games from 2001 to 2011 and found that there was not sufficient evidence on whether mobile games improved learning outcomes. Similarly, Cheung and Hew (2009) reviewed studies on mobile devices from 2000 to 2008 and found no significant differences in students' test scores for studies that compared mobile devices to equivalent paper-and-pencil treatments. They also reported that claims of enhanced learning were often not experimentally tested. Although there have been several valuable syntheses of previous studies on mobile learning, there are areas that require further examination. For example, there is strong potential for using mobile learning in the area of science education due to a number of aspects that make it unique and well suited to the affordances of mobile technology. Much of science takes place outside of the classroom and is arguably better studied in its natural environment, while other science content is impossible to see with the naked eye and requires graphical visualizations for students to be able to fully understand it. In addition, scientific system models cannot be completely comprehended without an immersive experience that demonstrates how the variables interact. These distinct aspects of science learning are well aligned with the mobility of newer devices as well as their ability to display interactive, three-dimensional graphics and simulations. However, there have been no reviews of research conducted to date on mobile learning in science. Furthermore, only a few studies reviewed the attributes or design patterns/features of mobile apps (Avouris & Yiannoutsou, 2012; Jeng et al., 2010; Schmitz et al., 2012), and two of these studies were focused specifically on games. Also, none of the studies on mobile learning thoroughly examined the specific theoretical foundations underlying the mobile learning research, although one review by Cheung and Hew (2009) noted that much of the research was not theoretically grounded. Given the mixed results on the effectiveness of mobile environments on learning outcomes, the potential of mobile learning in science education, and the absence of reviews focusing on design features and theoretical foundations of mobile applications, a review is needed to further examine the design and effectiveness of mobile applications being integrated into science education. Based on the areas that need further examination, the purpose of this review of research is to provide an updated review of studies on mobile apps, specifically in the area of science learning. The analysis framework used to guide the review was the concept of grounded learning systems design, “a process that involves linking the practices of learning system design with related theory and research” (Hannafin, Hannafin, Land, & Oliver, 1997, p. 101). This framework provided a lens through which to examine the literature for the connections made between the theoretical foundations, its corresponding design principles and features, and the validated research outcomes (Hannafin et al., 1997). To apply this framework, the review examined the literature for its alignment of the mobile app's design features, the underlying theoretical foundations, and the resulting outcomes related to science learning, as well as discussed their interrelationship with one another. This framework formed the basis for the research questions for this review, which are as follows: 1. What is common to the mobile app design used in science mobile app studies including: a) the general app characteristics? b) the specific design features? 2. What are the theoretical foundations common to mobile app studies in science? 3. What are the measured outcomes related to science learning associated with mobile app studies in science?

2. Method 2.1. Article selection To find articles for this review, the Web of Science (all databases) and SCOPUS databases were used to search for mobile learning in science education. The review covered articles published from 2007 (the introduction of the iPhone and other smartphones) to 2014. These databases were chosen because they are known for encompassing high impact, high quality journals indexed in the Science Citation Index and the Social Citation Index.1 Both databases were searched with the same

1 The methodology of article selection and analysis described here was adapted from a review of game-based learning in science conducted by Li and Tsai (2013).

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keywords: mobile, learning, and science. Related terms were used to create a more comprehensive search. For example, alternative keywords for mobile included ubiquitous and handheld; the alternative keywords for learning were instruction, teaching, and inquiry; and the alternative keywords for science were chemistry, biology, and physics. After duplicates were removed, 1518 articles were left for further selection. Titles and abstracts were reviewed to select papers that met the following criteria: 1) peer-reviewed journal articles, 2) available full text, 3) empirical research, 4) related to science learning, 5) included a mobile device, and 6) targeted an interactive mobile app. Several exclusion criteria were also applied: 1) used for professional learning (e.g., teacher education, engineering, or health professionals), 2) emphasized app design and development as opposed to student outcomes, 3) aimed exclusively at outcome measures unrelated to science learning (e.g., usability, engagement, interest), 4) directed at laptop use, robots, or wearable systems as opposed to tablets or phones, 5) focused on student-created apps, and 6) did not include an in-person component. The authors developed this criteria list based off established criteria used in earlier reviews (e.g., Li & Tsai, 2013; Wu et al., 2012), but then further narrowed the criteria while reviewing the articles in order to do a more focused, in-depth review of the studies. For example, several studies examined outcome measures unrelated to science learning, which was not the focus of the present article; thus, this exclusion criteria was added and applied to the article selection. By reviewing titles and abstracts, the articles were narrowed down to 113 articles. The second author independently reviewed approximately 20% of the articles to confirm the reliability of the coding method (Sulzer-Azaroff & Mayer, 1977). The inter-rater agreement was initially 93% and then after discussion was brought to 100% agreement. These articles were downloaded and methods sections reviewed to verify that the articles truly met all the criteria for inclusion in the review. The second author independently reviewed approximately 25% of this narrowed down list of articles, and the inter-rater agreement was initially 88% and then was brought to 100% after discussion. A total of 37 articles met the criteria to be included in the final review. 2.2. Analysis The articles that met the inclusion criteria were analyzed with a qualitative content analysis method, which is a systematic classification process for analyzing text into categories for the purposes of interpreting meaning (Hsieh & Shannon, 2005). Information from the articles relevant to the three research questions was coded and then classified into categories. Then, frequencies for each category were computed and reported in tables. Three strategies were used to establish trustworthiness and credibility of the analysis. First, the researchers had ongoing dialog to verify the categories and classification of information from the articles (Graneheim & Lundman, 2004). Second, a detailed explanation of the categories and themes that emerged as findings for each research question is provided in the Results section to provide transparency regarding how the €s, 2008; Hsieh & Shannon, 2005). Finally, examples for each category are provided to categories were created (Elo & Kynga demonstrate how well the categories represent the data (Graneheim & Lundman, 2004). 3. Results The results of the reviewed research studies are described in this section. First, an overview of the research is provided followed by the results of the three research questions focused on the themes of the mobile app design, theoretical frameworks underpinning the design of the mobile learning environments, and the resulting student learning outcomes of the studies. 3.1. Overview of reviewed studies Of the 37 studies reviewed from 2007 through 2014, the majority of studies (31 articles) were published after 2010. The participants of the studies were mostly elementary school students in general education (20 articles). Other studies focused on post-secondary students (3 articles), secondary school students (5 articles), middle school students (3 articles), or cut across multiple school levels (6 articles). Only a few studies focused on students who received either special education (3 articles) or gifted services (1 article). The number of student participants in the studies ranged from 10 to 1818 with the median of about 48. The majority of studies (29 articles) took advantage of the mobility of the devices and conducted at least part of the study in informal settings, such as a field trip or an outdoor location. Given that many studies on mobile apps were done in connection with a field trip, many studies (22 articles) covered a short instructional duration under 3 weeks. The national science standards (National Science Teachers Association, 2014), which are well established in science education, were used to classify the science topics covered in the studies into the following areas: life sciences (24 articles), earth sciences (5 articles), physical sciences (4 articles), multidisciplinary (3 articles), or unspecified (1 article). An overview of the research studies is provided in Table 1. 3.2. General characteristics of mobile apps This section addresses the first part of the research question on mobile app design, focusing on the general characteristics. There were 34 different mobile apps tested by the researchers. An overview of these mobile apps is provided in Table 2. Three apps were tested across multiple studies: the PDA version of CMapTools (http://cmap.ihmc.us/), an interactive tool for developing concept maps (Hwang, Shi, & Chu, 2011; Hwang, Wu, & Ke, 2011); a location-based, augmented reality (AR) app to

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Table 1 Background information on reviewed articles. Authors

School level

Gifted/ Special ed

Nbr. of students

Informal component of setting

Duration of instruction

Science domain

Ahmed and Parsons (2013) Chiang et al. (2014a) Chiang et al. (2014b) Chu, Hwang, and Tsai (2010) Chu, Hwang, Tsai, et al. (2010) Dekhane and Tsoi (2012) Dunleavy et al. (2009) Huang et al. (2010) Hung et al. (2013) Hung et al. (2012) Hung et al. (2014) Hwang, Chu, et al. (2011) Hwang, Chu, et al. (2010) Hwang, Kuo, et al. (2010) Hwang, Shi, et al. (2011) Hwang et al. (2012) Hwang, Wu, et al. (2011) Kamarainen et al. (2013) Laru et al. (2012) € m et al. (2013) Liljestro Lin et al. (2013) Looi et al. (2011) Looi et al. (2014) Marty et al. (2013) Perry and Klopfer (2014) Rosenbaum et al. (2007) Schneps et al. (2014) Sha et al. (2012) Song (2014) Song et al. (2012) Squire and Klopfer (2007) Squire and Jan (2007) S anchez and Flores (2008) Tan et al. (2007) Ward et al. (2013) Wong (2013) Case Study 2 Yang and Lin (2010)

Secondary Elementary Elementary Elementary Elementary Postsecondary Multi-level Elementary Multi-level Multi-level Middle Elementary Elementary Elementary Elementary Elementary Elementary Middle Elementary Multi-level Postsecondary Elementary Elementary Elementary Secondary Secondary Secondary Elementary Middle Elementary Multi-level Multi-level Postsecondary Elementary Secondary Elementary Elementary

e e e e e e e e e X e e e e e e e e e X e e e e e e e e e e e X X e e e e

161 57 57 13 57 97 80 32 49 48 86 41 42 50 70 43 30 71 22 17 40 39 1196 1818 239 21 152 67 28 37 76 28 10 72 30 NS 34

e X X X X e X X X X X X X X X X X X X X e X X X X X e e X X X X NS X e X NS

1 week 2 h 40 min 3h 2 h 20 min over 1 week 1h Not specified 100 h over 1 year 4h 3 trips over 4 months 3 trips over 3 months 80 min 5 h 20 min over 3 weeks 36 h over 18 weeks 2h 6 h 30 min 2h 4h 3 days 1 day 3 months 1h 21 weeks 4 years 3 weeks ~8 h over 4e8 weeks 2h 40 min over 2 days 5 weeks 1 year 3 weeks 2 to 3 h 2 to 3 h 3 h over 1 month 16 weeks 2 h 55 min over 2 days 1 year 3 h over 4 sessions

Physical Life Life Life Life Physical NS Life Life Life Life Life Life Life Life Multiple Life Earth Life Earth Physical Life Multiple Life Life Life Earth Physical Life Life Earth Earth Life Life Life Multiple Life

Total

NA

4

NA

29

NA

NA

Note. NS ¼ Not Specified.

guide students in inquiry-learning activities (Chiang, Yang, & Hwang, 2014a, 2014b); and GoKnow: GoMLE (http://goknow. com/), a mobile learning environment that helps manage a classroom and provides custom applications to help students reflect and illustrate their knowledge (Looi et al., 2011; Sha, Looi, Chen, Seow, & Wong, 2012; Song, Wong, & Looi, 2012; Wong, 2013). These 34 apps varied by their availability, science content, and type of app. 3.2.1. Availability The availability of the mobile apps seemed to be influenced by several factors, including the app developer, whether it was publicly accessible, and the recency of the platform. The majority of the mobile apps were developed for assessment in house by the researchers themselves (25 apps) as opposed to being created by outside companies. As a result, only 10 of the apps examined by the studies were accessible for use by the public through an internet/app store search. Over half of the mobile apps were developed for older platforms, such as PDAs or Handheld/Pocket PCs (18 apps), as opposed to more recent platforms such as smartphones, tablets, and iPods (15 apps). Many of these older platforms are no longer being used by the general public. 3.2.2. Science content The apps varied on whether science content was included in the app. Although there were a handful of apps that were content-free, most apps (27) either directly included content for science learning or provided a customizable template that was enriched with science content. An example of a customizable app was an app created by Hung, Hwang, and Wang (2014) that provides Quick Response (QR) codes to guide students to learn about plants. The template also includes a mind-mapping area for students to create a concept map and a problem-posing area for students to ask questions. This app could potentially

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Table 2 General characteristics of the mobile apps. App name

Developed by researchers

Publicly accessible

Recent platform

Science content enriched

App type

Researchers

Alien Contact AR Physics AudioNature CMapTools (PDA version) þ custom toolsa

X X X e

e e e X

e e e e

X X X X

S/G S S P

CIDAS Edmodo EULER Environmental Detectives Evernote Flyer Food Chain FreshAir: EcoMOBILE GoKnow: GoMLE

X e X X e X e e Unclear

e X e e X e X X X

e X e e X X X X X

X e X X e e X X e

P L L S/G Pr P G P L

Habitat Tracker Mad City Mystery MapHit Track MUKS MKC MPLS MyDesk No name (decision tree mechanism) No name (heuristic algorithm) No name (location based AR) No name (mind mapping and problem posing) No name (shared display groupware functionality) No name (three-layer inquiry mechanism) No name (two-tier test guiding mechanism) Outbreak @ Institute Skitch Solar Walk ThinknLearn TsoiChem UbiqBio USDT

X X Unclear X X X X X X X X

X e e e e e e e e e e

X e X e e e X e e X X

X X e X X X e X X X X

P S/G P P P P L P P P P

Dunleavy et al. (2009) Lin et al. (2013) S anchez and Flores (2008) Hwang, Shi, et al. (2011); Hwang, Wu, et al. (2011) Hung et al. (2012) Song (2014) Tan et al. (2007) Squire and Klopfer (2007) Song (2014) Laru et al. (2012) Ward et al. (2013) Kamarainen et al. (2013) Looi et al. (2011); Sha et al. (2012); Song et al. (2012); Wong (2013) Marty et al. (2013) Squire and Jan (2007) € m et al. (2013) Liljestro Hwang, Chu, et al. (2011) Chu, Hwang, and Tsai (2010) Huang et al. (2010) Looi et al. (2014) Hwang, Chu, et al. (2010) Hwang, Kuo, et al. (2010) Chiang et al. (2014a, 2014b) Hung et al. (2014)

X

e

e

X

P

Yang and Lin (2010)

X X

e e

e e

X X

P P

Hung et al. (2013) Chu, Hwang, Tsai, et al. (2010)

X e e X X X X

e X X X e e e

e X X NS X X e

X e X X X X X

S/G Pr S L S/G S/G P

Rosenbaum et al. (2007) Song (2014) Schneps et al. (2014) Ahmed and Parsons (2013) Dekhane and Tsoi (2012) Perry and Klopfer (2014) Hwang et al. (2012)

25

10

15

27

NA

Total

Note. S ¼ Simulation, G ¼ Game, L ¼ Learning Management System, P ¼ Place-Based Data Collection, Pr ¼ Productivity, NS ¼ Not Specified. a Although CMapTools is publically available, the authors also created some customized functions that are not available to the public.

be customized for a variety of different subjects by modifying the learning objects and associated content linked by the QR codes. 3.2.3. App type Four broad categories of apps surfaced from the analysis of the literature, including place-based data collection tools (17 apps), games and/or simulations (10 apps), learning management systems (LMS) (5 apps), and productivity tools (2 apps). Placed-based data collection tools were the most common type of app noted in this review. An example of this type of app is MapHit Track which uses Global Positioning System (GPS) to enable students to record field notes of their observations for the € m, Enkenberg, & Po €lla €nen, 2013). There were generally three types of games/ exact coordinates along their route (Liljestro simulations: immersive participatory games, ubiquitous games, and visual/audio simulations. In an immersive participatory game, a physical location is transformed into a fictitious place where players use their mobile devices to search for clues and information in order to solve a problem. An example of an immersive participatory game is Outbreak @ Institute, which simulates the outbreak of a disease on a campus, and players must work together to try to stop or contain the disease from spreading (Rosenbaum, Klopfer, & Perry, 2007). A ubiquitous game is a casual game for smartphones, such as the UbiqBio game called Beetle Breeders where players run a pet shop that breeds specific types of beetles based on customer needs (Perry & Klopfer, 2014). A visual/audio simulation provides a means for seeing something that isn't possible to see under normal

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circumstances. For example, the Solar Walk app is a visual simulation that allows users to explore a three-dimensional model of the solar system on their iPads (Schneps et al., 2014). Several apps provided a LMS for managing lessons and activities. One app that helps facilitate outdoor learning activities is the Environment of Ubiquitous Learning with Educational Resources (EULER), which includes a variety of tools on the PDA for managing discussions, classroom assignments and schedules, grading, data collection, and knowledge sharing (Tan, Liu, & Chang, 2007). Another example is the ThinknLearn app, which provides content management, assessment, and communication to help facilitate inquiry activities (Ahmed & Parsons, 2013). Although some LMS and place-based data collection apps include productivity tools, which allow students to create a variety of different types of documents as part of the app (e.g., MyDesk included a Sketchbook to create/enhance images), only a couple apps' main purpose was considered to be a generic productivity tool. For example, Song (2014) studied students' use of Evernote, a commercially available, productivity tool for note taking. 3.3. Mobile app design features This section addresses the second part of the research question on the mobile app design, concentrating on the design features. Despite the variability in mobile apps used for science learning, a number of similar categories of design features emerged from the literature, including technology-based scaffolding, location-aware functionality, visual/audio representations, digital knowledge-construction tools, digital knowledge-sharing mechanisms, and differentiated roles. These six design features were considered important to highlight in this review because they were a) common features to a number of the apps, and b) either addressed a common issue with mobile learning, such as reducing potential cognitive overload, or took advantage of the affordances of mobile technology. For each design feature described below, one or two examples of apps are given to illustrate the feature and, when possible, associated research that isolated that particular design feature is highlighted. An overview of these design features is provided in Table 3. 3.3.1. Technology-based scaffolding The most frequent design feature employed by the mobile apps was the use of technology-based scaffolding (23 apps). Many of these apps utilized scaffolding to address one of the common challenges with mobile learning, which is that students can become overwhelmed or overloaded by studying real-world issues with minimal supports (Dunleavy, Dede, & Mitchell, 2009; Rosenbaum et al., 2007). There were a variety of different types of scaffolds found in these mobile apps, which can be classified based on their functions: conceptual, metacognitive, procedural, and strategic (Hannafin, Land, & Oliver, 1999). Conceptual scaffolds provide guidance on the underlying concepts and knowledge to consider. Many of the apps provided hints, feedback, access to experts, and just-in-time resources to provide this type of guidance. One example of this type of scaffolding was an app that utilized two-tier guidance developed by Chu, Hwang, Tsai, et al. (2010). In this app, students are provided with personalized guidance to observe and classify plants through adaptive supports. In the first tier, students are asked to identify a feature of a plant and are guided to observe comparative plants to detect the differences in that feature. In the second tier, once the students correctly identify the feature, they are asked a more in-depth conceptual question and are provided with hints and supplementary materials as needed. Chu, Hwang, Tsai, et al. (2010) conducted a study to compare the two-tier guidance app with a tour-based app. Both apps guided learners to observe plants and provided related information about the plants, but the tour-based app did not provide the two-tier guidance. Elementary students who received the twotier guidance had significantly better conceptual knowledge about plant classifications than those who received the tourbased app (Chu, Hwang, Tsai, et al., 2010). Metacognitive scaffolds offer guidance to help students monitor and manage their learning (Hannafin et al., 1999). One example of an app that utilized metacognitive scaffolding was the MyDesk app (Looi et al., 2014). In the MyDesk app, a KWL tool prompts students to continually reflect on their learning by responding to the “questions (i.e. what do I already Know? what do I Want to know? What have I Learned?) to allow students to learn in a self-regulated way” (Looi et al., 2014, p. 105). In an earlier study on a similar app, Looi et al. (2011) observed that student questions in response to the W prompt reflected deep thinking, and one student commented that he enjoyed seeing how his learning progressed. Although these researchers did not specifically isolate this one design feature, they found that students who used the app, which contained the KWL prompts along with several other features, did significantly better on the end-of-year science exams than students who did not use this app (Looi et al., 2011). Future research that isolates the use of metacognitive scaffolds would be useful to confirm these results. Procedural scaffolds provide assistance with how to use features or perform certain tasks (Hannafin et al., 1999). One example of an app that provided this type of assistance was the Ubiquitous Scientific Device Trainer (USDT) developed by Hwang, Tsai, Chu, Kinshuk, and Chen (2012). The USDT app guided students to different scientific devices within a museum and gave instructions for how to operate each device. Students who used the USDT app were significantly better at applying their knowledge to problems than students who received an instructor demonstration of how to operate each device. This study found that this type of assistance provided personalized guidance to students because just-in-time information could be accessed as many times as needed (Hwang et al., 2012). Strategic scaffolds provide guidance on how to approach a task or problem (Hannafin et al., 1999). An example of an app with a strategic scaffold was ThinknLearn assessed by Ahmed and Parsons (2013). ThinknLearn app provides guidance through the abductive inquiry process: exploration, examination, selection, and explanation. In the exploration phase, the app prompts students to take measurements. In the examination phase, the app asks the students questions about the measurements taken. In the selection phase, the students select a possible hypothesis to explain their observations. And, in

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Table 3 Design features of the mobile apps. App name

Tech-based scaffolding

Location aware

Visual/ Audio

Digital knowledge construction

Digital knowledge sharing

Rolebased

Researchers

Alien Contact AR Physics AudioNature CMapTools (PDA version) þ custom tools CIDAS Edmodo EULER Environmental Detectives Evernote Flyer Food Chain FreshAir: EcoMOBILE GoKnow: GoMLE

e e e X

X e e X

e X X X

e e e X

e e e e

X e e e

X e X X e X e X X

X e X X e X e X e

X e e e e e X X X

X X X e X X e e X

e X X e X X e e e

e e e e e e e e e

Habitat Tracker Mad City Mystery MapHit Track MUKS MKC MPLS MyDesk No name (decision tree) No name (heuristic algorithm) No name (location-based AR) No name (mind mapping/ problem posing) No name (shared display groupware functionality) No name (three-layer inquiry) No name (two-tier test guiding (T3G)) Outbreak @ Institute Skitch Solar Walk ThinknLearn TsoiChem UbiqBio USDT

X X e X X e X X X X X

e X X X X X e X X X X

e e X X X e X e X X X

X e X X X X X e e X X

X e X X e X X e e X e

e X e e e e e e e e e

Dunleavy et al. (2009) Lin et al. (2013) S anchez and Flores (2008) Hwang, Shi, et al. (2011); Hwang, Wu, et al. (2011) Hung et al. (2012) Song (2014) Tan et al. (2007) Squire and Klopfer (2007) Song (2014) Laru et al. (2012) Ward et al. (2013) Kamarainen et al. (2013) Looi et al. (2011); Sha et al. (2012); Song et al. (2012); Wong (2013) Marty et al. (2013) Squire and Jan (2007) € m et al. (2013) Liljestro Hwang, Chu, et al. (2011) Chu, Hwang, and Tsai (2010) Huang et al. (2010) Looi et al. (2014) Hwang, Chu, et al. (2010) Hwang, Kuo, et al. (2010) Chiang et al. (2014a, 2014b) Hung et al. (2014)

X

e

e

X

X

X

Yang and Lin (2010)

X X

X X

e e

X e

e e

e e

Hung et al. (2013) Chu, Hwang, Tsai, et al. (2010)

e e e X X X X

X e e e e e X

e X X e X X e

e X e X e e e

e X e e e e e

X e e e e e e

Rosenbaum et al. (2007) Song (2014) Schneps et al. (2014) Ahmed and Parsons (2013) Dekhane and Tsoi (2012) Perry and Klopfer (2014) Hwang et al. (2012)

Total

23

20

18

19

12

4

the explanation phase, students are prompted to provide complete explanations for the given problem. Ahmed and Parsons (2013) found that high school students who used the ThinknLearn app retained significantly more knowledge about energy transfer and demonstrated more critical thinking in their hypothesis generation than a control group that did not receive the ThinknLearn app. One student commented in an interview that the app kept him focused during scientific tasks (Ahmed & Parsons, 2013). 3.3.2. Location-aware The next most common design feature employed by many of the apps was a location-aware feature (20 apps). These apps took advantage of the mobility of the devices by enabling the app to detect the users' location and provide different information or clues depending on where the user is within a physical space. This can be accomplished by using GPS to determine where the user is located or by placing Radio Frequency Identification (RFID) tags, QR Codes, or Bluetooth iBeacons at a specific location (e.g., on a plant). This design feature is common to a number of different types of apps, such as place-based data collection apps, immersive participatory game apps, and some LMS apps. Several studies compared an app with a location-aware feature to a paper-based worksheet or guide (Huang et al., 2010; Hung, Hwang, Lin, Wu, & Su, 2013; Hwang et al., 2012), and two studies isolated this location-aware feature to test its effectiveness (Chiang et al., 2014a, 2014b). For example, Chiang et al. (2014a, 2014b) conducted quasi-experimental studies to assess an app that guided students through an inquiry-based activity to study aquatic plants. In both studies, the experimental group received a version of this app enhanced with a location-aware feature that utilizes GPS technology to guide students to specific areas and provide them with

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associated activities or related content, and the comparison group did not receive this feature. These researchers found that this location-aware feature significantly improved students' learning achievement (Chiang et al., 2014a) and increased their level of knowledge construction (Chiang et al., 2014b). However, this feature can sometimes be unstable, such as when the network goes down or GPS signal is lost, and this can reduce students' enjoyment of using the tool for outdoor learning (e.g., Huang et al., 2010). Overall, the advantage of apps that include a location-aware feature appears to be greater convenience, allowing for better learning outdoors, but this functionality also increases the potential for technical problems to occur in the field. 3.3.3. Visual/audio representations Many of the apps took advantage of the multimedia capabilities of mobile devices by providing visual and/or audio representations of information (18 apps). Several of these apps provided a method for students to create a visual representation of the information they obtained through a concept map (e.g., Hung, Hwang, Su & Lin, 2012), a knowledge grid (e.g., Chu, Hwang, & Tsai, 2010) or an illustrated animation (e.g. Looi et al., 2011). To assess the effect of creating these types of visual representations, Hwang et al. (2011) compared three different groups: one group that used an app with CMapTools to develop concept maps on their PDAs; one group that created paper-and-pencil concept maps; and one group that did not create a concept map at all. All groups used the PDAs with customized tools to help guide them to find the butterflies and to provide them with the learning task and related resource materials. The students who used the CMapTools did significantly better on their knowledge of the characteristics of butterflies than either the group that created the paper-and-pencil concept map or the group that didn't create a concept map. There was no significant difference in knowledge gained between the paper-and-pencil concept map group and the no concept map group. It would be interesting for future research to compare the effect of students creating different types of visual representations (e.g., a concept map versus an animated illustration) on their learning. Other apps presented information to students through visual or audio effects that allow users to see or hear things which they normally wouldn't be able to without such assistance, such as chemical structures (Dekhane & Tsoi, 2012), the solar € m et al., 2013). For example, one system (Schneps et al., 2014), or a geographic map (Hwang, Kuo, Yin, & Chuang, 2010; Liljestro app called AudioNature uses audio to help people with visual impairments create a mental model of biological concepts (S anchez & Flores, 2008). They found that most users made gains in cognitive tasks, although due to the small sample size, this was not analyzed statistically. A couple of studies specifically isolated different types of visual representations. For example, Schneps et al. (2014), in evaluating the Solar Walk app, found that true-to-scale visualizations of the solar system were significantly better than orrery visualizations, which exaggerate the scale in order to emphasize the surface features of the planets, in improving students' understanding of the conceptual scale of the solar system, an area in which students often have strong misconceptions. And, Lin, Duh, Li, Wang, and Tsai (2013) assessed an app called AR Physics and found that threedimensional representations are significantly more effective than two-dimensional representations in improving students' knowledge of elastic collisions. 3.3.4. Digital knowledge construction Over half the apps gave users an opportunity to digitally construct knowledge (19 apps). For example, Chu, Hwang, and Tsai (2010) developed a Mobile Knowledge Constructor (MKC) to assist students in guiding them to observe plants in order to develop a grid to compare and classify different plants. The MKC provides students with feedback and hints to assist them in developing their grid. Chu, Hwang, and Tsai (2010) compared elementary school students' use of the MKC app compared to a group that used a worksheet. Although there was no difference between the groups in basic factual knowledge of plants, students who used the MKC app demonstrated significantly better classification and comparison abilities. The researchers speculated that the reason for the non-significant difference for the factual knowledge might be due to the small sample size. These researchers found that MKC was effective for individual learning, but future research is needed to determine how this could be used with cooperative learning (Chu, Hwang, & Tsai, 2010). 3.3.5. Digital knowledge sharing Although a little less common, one design feature to foster collaboration that some of the apps employed was providing tools for sharing knowledge digitally (12 apps). These apps tried to address the issue that mobile devices tend to promote more individual learning as opposed to team learning (Huang et al., 2010). Most knowledge-sharing apps allowed students to collect and share information while outside with their mobile device and then use a computer to analyze the shared information back in the classroom. To assess the effectiveness of a knowledge-sharing feature, Hwang, Chu, Lin, and Tsai (2011) compared a Mobile Ubiquitous Knowledge Sharing (MUKS) app to an app that provided similar guidance and materials without this feature. Elementary students used the MUKS app on their PDAs to create knowledge grids for classifying butterflies based on field observations; to digitally share, integrate, and modify their knowledge grids on the classroom computer; and then to test out their modified knowledge grids by making additional observations in the field with their PDAs. The students in the comparison group used an app to guide them in their observations but completed a worksheet to record their observations and then orally shared their findings in the classroom. Students who used the MUKS app did significantly better than the comparison group on distinguishing between different butterflies (Hwang, Chu, et al., 2011). However, this study did not assess students' collaboration.

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More recently, two studies examined how students collaborate digitally within small teams and larger classes while using apps that include a knowledge-sharing feature (Chiang et al., 2014b; Song, 2014). Song (2014) examined cooperative team learning by doing an in-depth analysis of one team who used Edmodo and Evernote to share information. One finding was that the students compared individual results in order to form generalized conclusions. Chiang et al. (2014b) analyzed whole class discussions in an online chat room tool within a location-based AR app. Through a detailed content analysis of the discussions, the researchers noted common interaction patterns. For example, in one pattern, students shared their opinions, engaged in in-depth comparisons, attempted to consolidate opinions to form conclusions, and then re-evaluated their ideas when finding problems with their initial solution. One suggestion for future research is to examine the use of intelligent technology that can help monitor online discussions, provide timely feedback to students, and prompt teachers when assistance is needed to redirect the discussion (Chiang et al., 2014b). 3.3.6. Differentiated roles A feature found in a few of the apps is the use of differentiated roles (4 apps). Although not as common, it is mentioned here because it may become more common in the future as it is also used to enhance team learning. These apps provide differentiated roles that give varying information, clues, or tasks to different students on a team so that they are required to share information with one another. For example, in Mad City Mystery, players become medical doctors, environmental specialists, and government officials, who have different abilities in the game and must work together to solve a mystery of a fictitious character's death (Squire & Jan, 2007). For example, medical doctors may diagnose patients and obtain medical histories; whereas, government officials have access to documents about the local toxins in the area where the character died. Although none of the researchers isolated the use of differentiated roles in comparison to non-differentiated roles, the researchers who studied apps with this feature found qualitative evidence through observations, interviews, or surveys that the different roles were helpful in promoting collaboration because each role needed to rely on one another for information (Dunleavy et al., 2009; Rosenbuam et al., 2007; Squire & Jan, 2007; Yang & Lin, 2010). One potential downside of this approach is that if a student is absent and a role is missing on a team, it can disable the team from being able to play the game effectively (Dunleavy et al., 2009). On the other hand, without these differentiated roles, dominant personalities or other social factors can cause the teams to be unbalanced (Squire & Klopfer, 2007). Given the positives of using differentiated roles to promote collaboration and distributed knowledge, future research is needed to figure out strategies for how best to design teams to not be completely dependent on each role being present (Dunleavy et al., 2009).

3.4. Theoretical foundations This section discusses the research question on theoretical foundations. Underlying the design of many of the science mobile apps and their surrounding learning environments were theoretical foundations of learning that ranged from broad learning perspectives to specific instructional principles. However, distinctions can be made regarding the extent to which theory informed the design of the mobile apps. Using the grounded learning systems design model as a lens through which to view the articles (Hannafin et al., 1997), three categories arose from the literature based on the degree to which theory was applied in the design of the mobile app being studied: grounded, cited, and theoretical foundation not provided. In order to be considered grounded, the authors needed to cite specific theorists and their theory, as well as explicitly describe how that theory informed the design of the mobile app. Studies that cited a theoretical foundation but did not explicitly apply the principles of the theory to the design of the mobile app were categorized as cited. Finally, studies that did not cite specific theorists or apply the principles of a theory were categorized as not provided. Table 4 provides a summary of theoretical foundations discussed in the articles, as well as their connection to the design of the mobile apps. As shown in the “Theory Use” column of Table 4, most articles involved an application grounded in at least one theory (25 articles); however, several articles only described a theory without explicitly applying its principles to the design of a mobile app (5 articles) or did not cite a theorist and/or theory (7 articles). 3.4.1. Grounded theoretical foundations Mobile apps were most frequently grounded in situated learning theory (8 articles). These studies often cited the work of Brown, Collins, and Duguid (1989) and emphasized the importance of connecting learning to an authentic context in which it can be used. For example, Hwang, Chu, Shih, Huang, and Tsai (2010) applied situated learning theory to their design of a mobile app that guided students in studying butterflies and plants in their natural habitat. The next most frequently grounded theoretical foundation was inquiry-based learning (6 articles). In a study by Ahmed and Parsons (2013), a specific inquirybased learning approach, the abductive inquiry model, was applied in the design of their ThinknLearn app, which supported students' hypothesis testing within a physics lab. Several studies included applications grounded in Vygotsky's (1978) sociocultural theory (3 articles) and/or the associated instructional principle of scaffolding (5 articles). Studies that cited these theoretical foundations often discussed Vygotsky's (1978) concept of the zone of proximal development (i.e., the difference between a person's current level of ability and the level that he or she can possibly attain with support), as well as Wood, Bruner, and Ross's (1976) concept of scaffolding, a method of tiering support that enables a learner to reach their level of potential. For example, Laru, Jarvela, and Clariana (2012) applied both sociocultural theory and scaffolding in the design of their Flyer app, which included a tutor function that provided both procedural and metacognitive support.

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Table 4 Theoretical foundations of mobile learning environments. Authors

Theory use

Situated learning

Inquiry-based learning

Sociocultural theory

Scaffolding Communities of practice

Seamless learning

Other

Ahmed and Parsons (2013) Chiang et al. (2014a) Chiang et al. (2014b) Chu, Hwang, and Tsai (2010) Chu, Hwang, Tsai, et al. (2010) Dekhane and Tsoi (2012) Dunleavy et al. (2009) Huang et al. (2010) Hung et al. (2013) Hung et al. (2012) Hung et al. (2014) Hwang, Chu, et al. (2011) Hwang, Chu, et al. (2010) Hwang, Kuo, et al. (2010) Hwang, Shi, et al. (2011) Hwang et al. (2012) Hwang, Wu, et al. (2011) Kamarainen et al. (2013) Laru et al. (2012) € m et al. (2013) Liljestro Lin et al. (2013) Looi et al. (2011) Looi et al. (2014) Marty et al. (2013) Perry and Klopfer (2014) Rosenbaum et al. (2007) Schneps et al. (2014) Sha et al. (2012) Song (2014) Song et al. (2012) Squire and Klopfer (2007) Squire and Jan (2007) S anchez and Flores (2008) Tan et al. (2007) Ward et al. (2013) Wong (2013) Case Study 2 Yang and Lin (2010)

X X X X X X X / X e / e X X X X / X X X e / X X X / e X X X X X e X e X e

e e e X X e X e e e / e X e X e e X e X e e e e e / e e e e X e e e e e e

X X X e e e e e / e e e e e e / e e / e e e X X e e e e X e e / e e e e e

e e e e e e e e e e e e e e X X e e X / e e e e e e e / e e e e e e e e e

e e e e e X e e X e e e e e e X e e X e e e e e X e e e e e e e e e e e e

e e e e e e / e e e e e / e e e e e e X e e e e e / e e e e / e e e e e e

e e e e e e e e e e e e e e e e e e e e e / X e e e e e / / e e e e e X e

X X X X e X e / X e e e e X / e / e e X e e e e X e e Xa e Xb X X e X e e e

Grounded Overall

25 30

8 10

6 10

3 5

5 5

1 5

2 5

17 22

Note. X ¼ Grounded, / ¼ Cited, e ¼ Not Provided. a Study cited more than one theoretical foundation or principal theorist but did not ground all theories to the design of the mobile learning environment. b Study grounded more than one theoretical foundation to the design of the mobile learning environment.

Several other theories were used on a limited basis in the literature to ground the design of a mobile app (included in the “Other” column of Table 4). For example, Mayer's (2001) cognitive theory of multimedia learning (2 articles), which describes principles for the integration of text, images, and video, was applied by Chiang et al. (2014a) in the design of their locationbased AR app. Closely related to Mayer's theory is Sweller's (2005) cognitive load theory (2 articles). To reduce extraneous cognitive load, Hung et al. (2013) designed their app to break learning activities into smaller tasks, provide direct instruction on an as-needed basis to reduce the need to search for information, and offer step-by-step guidance to enable students to focus on the learning task. Additional theoretical foundations discussed in at least two reviewed articles included problembased learning (Squire & Klopfer, 2007; Tan et al., 2007) and personal construct theory, a theory by Kelly (1955) used by researchers to organize knowledge into separate constructs (Chu, Hwang, & Tsai, 2010; Hwang, Kuo, et al., 2010). 3.4.2. Cited theoretical foundations Several theoretical foundations were cited without being applied directly to the design of a mobile app. For example, multiple studies cited Lave and Wenger's (1991) communities of practice (5 articles), which is the concept that authentic learning best occurs organically among groups of people with shared interests and needs. The majority of these articles (4) did not apply the model to the design of the application being studied. These studies often briefly mentioned the community of practice model, but typically utilized other frameworks like situated learning theory more heavily in the design of their app. Similarly, seamless learning, which prescribes how to use mobile technologies to bridge formal and informal learning environments (Looi et al., 2011), was described (3 articles) more often than it was applied (2 articles). In addition, social constructivism was cited in two articles but was not applied to the design in either study (included in the “Other” column of Table 4). Several of the theories mentioned in the grounded theoretical foundations section were also cited in several articles

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but not applied to the mobile app design, including situated learning (2 articles), inquiry-based learning (4 articles), and sociocultural theory (2 articles). 3.4.3. Theoretical foundations not provided Several articles did not cite a theorist or explicitly apply a theory to the design of the mobile app being studied. Although some articles included in this category made no mention of a theorist or theory, many of these articles did use language commonly associated with specific theories. For example, multiple studies described learning environments as being authentic, but did not cite situated learning theory or its associated theorists. 3.5. Student outcome measures This section focuses on the research question regarding student outcome measures. Studies on mobile apps in science education measured a variety of student learning outcomes. At least one learning outcome was found to be statistically significant in approximately 87% of the studies reviewed (excluding the six studies which analyzed qualitative data). Using the domains of learning (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956; Harrow, 1972; Krathwohl, Bloom, & Masia, 1964), these learning outcomes were categorized into two primary groups: cognitive and skill-based outcomes. These two categories were chosen because they are well established in the education literature and relevant to science learning. Table 5 highlights the student outcomes measured across the reviewed studies. Note: the affective domain was not included because it was not a focus of this review, given the consistent findings among prior reviews that mobile learning is motivating, engaging, and of interest to students (Hsu & Ching, 2013; Hwang & Wu, 2014; Schmitz et al., 2012). 3.5.1. Cognitive outcomes The cognition category was broken down into three areas: lower-level cognitive outcomes, which reflect what Bloom et al. (1956) described as knowledge and comprehension; higher-level cognitive outcomes, which align with what Bloom et al. classify as analysis, synthesis, and evaluation; and cognitive load, which describes factors that affect cognition. Within the lower-level cognitive outcomes, the most common outcome measured in this review was basic scientific knowledge or conceptual understanding (32 articles). These studies typically used a pre-posttest format, with multiplechoice or short-answer questions to assess knowledge gains. The questions were usually either borrowed from a curriculum, pulled from a standardized test, or created by experienced teachers. Assessing conceptual understanding via tests is typically quick to administer and easy to analyze, which may have contributed to the prevalence of this type of outcome in the literature. A second lower-level cognitive outcome, knowledge retention, was only observed in two studies (included in the “Other” column of Table 5). Knowledge retention was assessed quantitatively by administering a delayed posttest about 2 months after the instructional period concluded (Ahmed & Parsons, 2013; Hung et al., 2012). For example, Ahmed and Parsons (2013) found in a follow-up test that although all participants retained the scientific concepts to a certain degree, those who used their ThinknLearn app retained the information more than students who did not. Within the higher-level outcomes measured, the most frequent was knowledge construction/synthesis (7 articles), which was typically assessed through analyzing discussions or student-created artifacts. For example, Squire and Klopfer (2007) qualitatively assessed students' problem constructions by analyzing videotaped observations, focus groups, exit surveys, and artifacts from Environmental Detectives, an immersive game that puts students in the role of environmental engineers to investigate a chemical spill. Students tended to try to construct the problem based on incomplete or incorrect data. Based on these findings, the researchers recommended the use of additional metacognitive scaffolding to support students in constructing the problem. Studies that assessed students' artifacts for conceptual understanding, as opposed to construction or synthesis of knowledge, were not categorized as a higher-level outcome. Knowledge application/transfer (Hwang et al., 2012) was also seen on a limited basis in the literature (included in the “Other” column of Table 5). A few studies measured cognitive load, which is often used to explain why learners aren't retaining information. Cognitive load was measured twice using some type of quantitative instrument (Chiang et al., 2014a; Hwang, Wu, et al., 2011) and once through qualitative observations (Dunleavy et al., 2009). Findings related to cognitive load were inconsistent, with one study citing cognitive overload when using a mobile device as being problematic (Dunleavy et al., 2009), and two studies finding no difference between experimental and control groups (Chiang et al., 2014a; Hwang, Wu et al., 2011). This difference in findings on cognitive load may be the result of varying levels of technology-based scaffolding within the mobile apps. 3.5.2. Skill-based outcomes Frequent skill-based outcomes measured included scientific process skills (8 articles) and self-directed learning skills (4 articles). Studies examining scientific process skills focused on competences, such as developing research questions, generating hypotheses, making observations, or developing explanations. For example, Hung et al. (2012) assessed students' observation skills through a Computerized Ecology Observation Competence Assessment (CEOCA). This study found that students' observation competence significantly increased over time as students used the Concept Map Integrated Dynamic Assessment System (CIDAS) app to observe the ecology of the mangrove wetlands. Self-directed learning skills were assessed by examining the students' ability to complete self-regulated, personalized, or self-disciplined learning. For example, Song et al. (2012) assessed whether the GoKnow: GoMLE app fosters students' personalized learning by examining a variety of students' artifacts, responses, website histories, and videos and pictures taken during field trips. They found that students

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Table 5 Measured outcomes of studies. Authors

Cognitive outcomes

Skill-based outcomes

Other

Scientific knowledge/ concept

Knowledge construction/ synthesis

Cognitive load

Scientific process skills

Selfdirected learning

Problem solving

Ahmed and Parsons (2013) Chiang et al. (2014a) Chiang et al. (2014b) Chu, Hwang, and Tsai (2010) Chu, Hwang, Tsai, et al. (2010) Dekhane and Tsoi (2012) Dunleavy et al. (2009) Huang et al. (2010) Hung et al. (2013) Hung et al. (2012) Hung et al. (2014) Hwang, Chu et al. (2011) Hwang, Chu et al. (2010) Hwang, Kuo et al. (2010) Hwang, Shi, and Chu (2011) Hwang et al. (2012) Hwang, Wu, and Ke (2011) Kamarainen et al. (2013) Laru et al. (2012) € m et al. (2013) Liljestro Lin et al. (2013) Looi et al. (2011) Looi et al. (2014) Marty et al. (2013) Perry and Klopfer (2014) Rosenbaum et al. (2007) Schneps et al. (2014) Sha et al. (2012) Song (2014) Song et al. (2012) Squire and Klopfer (2007) Squire and Jan (2007) S anchez and Flores (2008) Tan et al. (2007) Ward et al. (2013) Wong (2013) e Case Study 2 Yang and Lin (2010)

X X e X X X e X e X X X X X X X X X X X X X X e X X X X X X e X X X X X X

e e X e e e e e e e e e e e e e e e X X X e e e e e e e X e X e e X e e e

e X e e e e X e e e e e e e e e X e e e e e e e e e e e e e e e e e e e e

X e X e e e e e X X e e e e e e e e e X e e e X e e e e e e X X e e e e e

e e e e e e e e e e e e e e e e e e e e e X e e e e e X e X e e e e e X e

e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e X e X e e

X e e e e e e e e X X e e e e X e e e e e e e X e X e X X e e X e e e e e

Total

32

7

3

8

4

2

9

took different learning paths, had differentiated learning goals, and worked at their own pace. Problem-solving was the only other skill-based outcome addressed in multiple articles (2). Several additional measures were occasionally assessed by studies (included in the “Other” column of Table 5), such as digital-literacy skills (Marty et al., 2013), collaboration and communication skills (Rosenbaum et al., 2007), social interaction skills (Squire & Jan, 2007), and metacognitive skills (Song, 2014). 4. Discussion A discussion of the reviewed research studies is provided for the three research questions: the mobile app design, the theoretical foundations underlying the mobile learning environments, and the resulting student outcome measures. The final section of the discussion highlights additional areas needed for future research. 4.1. Mobile app design There were a number of areas noted for future research within the area of mobile app design, including the general characteristics as well as the specific design features. These fell under three main themes: use of newer, available technologies; strategies around collaborating through mobile devices; and isolation of app features. 4.1.1. Use of newer, available technologies As noted in previous reviews on mobile learning (Hsu & Ching, 2013; Wu et al., 2012), the majority of studies reviewed were still focused on apps developed for older mobile platforms, such as the PDA. However, all studies in this review

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published after 2014 examined apps designed for the latest platforms. Thus, there may be a shift happening, where research is just beginning to catch up with the newer devices that provide the latest capabilities, such as pinch-and-zoom navigation and three-dimensional graphics. Likely because many of the studies utilized older platforms, there were a number of technical problems reported with the mobile devices, such as small screen size (Hwang, Wu et al., 2011), difficulty seeing the screen due to reflections of light (Dunleavy et al., 2009; Yang & Lin, 2010); connectivity problems (Dunleavy et al., 2009; Huang et al., 2010; Ward et al., 2013), difficulty typing (Huang et al., 2010); issues with hearing the audio in noisy outdoor environ€ m et al., 2013). As the trend of using newer devices ments (Dunleavy et al., 2009); and devices not working properly (Liljestro seemed to have shifted in 2014, the number of reported technical issues have also diminished. Most of the technology issues reported were more about inequitable access to technology (e.g., Perry & Klopfer, 2014; Song, 2014) as opposed to technology failures. It may be that these issues became less problematic as newer devices have become more robust and cellular service has becomes more abundant, as suggested by Dunleavy et al. (2009). Although there appears to be a positive trend in seeing more research on newer mobile platforms, there continues to be an issue with the availability of the apps being researched. The vast majority of the apps reviewed in this study were not publically accessible. In order for educators and other researchers to more fully utilize the research being done on mobile learning, it would be beneficial for these parties to be able to use the apps being investigated. In addition, although there were some studies that assessed the efficacy of commercially available apps developed outside the research community (e.g., Kamarainen et al., 2013; Song, 2014), more research is needed in this area as these are the apps that teachers are more likely to use in their classrooms. Finally, it would be interesting to see more studies on the use of generic productivity apps for science learning. 4.1.2. Collaboration strategies One issue that a number of studies attempted to overcome is how to effectively use mobile devices for collaboration by providing knowledge-sharing features (Chiang et al., 2014a; 2014b; Hwang, Chu et al., 2011; Song, 2014; Yang & Lin, 2010) and including differentiated roles (Dunleavy et al., 2009; Rosenbaum et al., 2007; Squire & Jan, 2007; Yang & Lin, 2010). However, more research is still needed in this area. In particular, it is necessary to determine how mobile apps can be designed to help students co-construct knowledge (Chu, Hwang, & Tsai, 2010). Moreover, assessing the use of intelligent technology that can help monitor online discussions to help students stay on track may be an interesting area for future research (Chiang et al., 2014b). Finally, when using differentiated roles, future research is needed to figure out a design that is not dependent on each role being present so that absences or uneven numbers in each team do not present a problem (Dunleavy et al., 2009). 4.1.3. Isolating design features This review attempted to look at the effectiveness of various design features of mobile apps, such as technology-based scaffolding, location-aware functionality, visual/audio representations, digital knowledge-construction tools, digital knowledge-sharing mechanisms, and differentiated roles. However, in some cases, this became difficult to do because there were no studies available that isolated a particular design feature, making it impossible to discern the effectiveness of a single feature. For example, more research is needed to isolate the use of metacognitive scaffolds like KWL prompts on students' learning. Another area noted for additional research is assessing whether having students create different types of visual representations (e.g., a concept map versus an animated illustration) has differing effects on their learning. Finally, although qualitative research has provided some evidence that differentiated roles may be helpful for teams, it would be beneficial to isolate the effect of using differentiated roles within a mobile learning environment on students' collaboration by comparing it to an app that does not provide this feature. 4.2. Theoretical foundations This review found that the majority of studies cited a theoretical foundation, and many of these studies applied that foundation to the design of the mobile app. This finding differed from a review by Cheung and Hew (2009) who examined studies on mobile devices from 2000 to 2008 and noted, “that a majority of the studies tended to place greater emphasis on the features of the mobile devices and procedures for using them, rather than on the theoretical rationale or justification for using them” (p. 166). This may indicate a positive improvement in the application of theory within app design, although a direct comparison to prior reviews is not possible because of different inclusion/exclusion criteria used. Although the design of many apps in this review were grounded in theory, a number of studies did not explicitly cite a theory or model. Instead, a more general overview of a learning perspective was provided, or terminology was used that implied an underlying framework. For example, some studies used the terms “authentic” or “situated” to describe a design without explicitly referencing situated learning theory or citing Brown, Collins and Duguid (1989) or other situativists. Other studies were not explicit about the instructional principles used to guide the design or did not fully implement the principles in their design. For example, several of the studies implemented some type of scaffolding in the design of their mobile app. An important principle of scaffolding is the notion of fading the support provided so that the learner can eventually complete the task independently (Pea, 2004), but none of the studies in this review examined fading the supports provided. Thus, the design principles for scaffolding were not fully implemented in these studies, making the connection between the design principle and the learning outcomes more ambiguous. To better understand how theoretical frameworks influence mobile designs in particular settings, researchers need to make explicit connections between the principles and the design features of their mobile learning environment and then test the designs to see if the underlying theories adequately describe how students

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learn in that particular environment. This will enable researchers to improve upon both their mobile design, as well as the underlying theory as recommended by design-based research (Wang & Hannafin, 2005). 4.3. Student outcome measures In closely examining students' learning outcomes measured in the science mobile learning literature, there was often a lack of alignment between the underlying theoretical framework or learning issue and the studies' measured outcomes. For example, many of the studies grounded their mobile design in situated learning. One of the intended benefits of situated learning is improved retention of knowledge and learning transfer because learning within an authentic context helps the learner form stronger connections with later situations when that knowledge might be useful (Bransford, Brown, & Cocking, 2000). However, only two studies measured knowledge retention (Ahmed & Parsons, 2013; Hung et al., 2013). The misalignment between learning issues and measured outcomes is also apparent in the discrepancy between the number of researchers who noted that mobile learning can be overwhelming to students without proper supports and the infrequent measure of students' cognitive load (Chiang et al., 2014a; Dunleavy et al., 2009; Hwang, Wu et al., 2011). Hwang and Wu (2014) observed a similar trend and suggested that it is important to assess possible negative effects of using mobile devices, such as loss of concentration or increase of cognitive load. It is recommended that future research on mobile apps focus on learning outcomes that correspond with the learning theories underlying the design of the applications, as well as the learning issue being addressed. 4.3.1. Cognitive outcomes With regards to cognitive outcomes, there was a large discrepancy between the number of articles measuring lower-level outcomes like basic knowledge of scientific concepts compared to higher-level cognition such as knowledge construction and synthesis. This could be due in part to the more time-intensive methods needed to assess the higher-level outcomes. Future studies should attempt to emphasize potential effects of mobile apps on higher-level cognitive outcomes through the use of methods and instrumentation aligned with the nature of these constructs. The measure of cognitive outcomes may have also been complicated by limitations of the research settings. For example, the durations of the majority of studies in this review was less than a few weeks, making it infeasible to collect knowledge retention data; a point also noted as an issue in a review by Cheung and Hew (2009). In addition, only one study in the review measured whether students could transfer the knowledge obtained to other situations (Hwang et al., 2012). More longitudinal designs and studies spanning multiple settings are necessary to provide a clearer picture of how mobile apps can influence these learning outcomes. As previously mentioned, measuring cognitive load should also be a focus in future studies involving the use of mobile applications, especially given the varied results reported in this review. 4.3.2. Skill-based outcomes Skill-based outcomes focused most frequently on components of scientific inquiry, such as generating hypotheses and developing explanations. Interestingly, very few articles measured outcomes related to problem solving. Given that the inherent purpose of scientific process skills is to solve problems, more studies utilizing procedures that allow researchers to measure problem-solving skills would be beneficial. Additional research is also needed in order to determine how mobile apps can serve as problem-solving tools throughout the scientific process in addition to scaffolds or supports. Provided the connectivity affordances of mobile apps, the infrequent measure of communicative or collaborative skills was also an interesting finding. It is possible that the use of older devices in a number of studies may have contributed to this result. Research focusing on how mobile apps affect communication in groups during science activities will be necessary moving forward in order to properly leverage this design feature. 4.4. Other areas for future research In addition to the main research questions, there were a few additional areas noted for possible future research within science mobile learning. For example, most studies in this review were focused on elementary students. Moreover, only a few studies in this review focused on gifted or special education populations. Thus, there is a need for future research to examine how mobile learning environments can be used with more diverse populations of students. In addition to studying more diverse students, there is a need to examine how mobile learning can be used with more varied topics. The majority of studies in this review focused on life sciences. Thus, there is a need for more research on mobile learning in the area of earth and physical sciences as well as multidisciplinary topics. This recommendation corroborates with a recent review by Hwang and Wu (2014) who noted that there is a need for mobile learning research in the areas of astronomy, physics, and chemistry. 5. Limitations This review was limited by examining articles indexed in two databases: the Web of Science and SCOPUS databases from 2007 to 2014. The articles in these databases are considered to have a high impact on the field; however, they may not reflect the most recent research as it takes a couple of years for articles to be published in top-ranking journals. The results and recommendations of this review reflect research with a strong reputation, but this may limit the findings to those studies that

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demonstrated statistically significant findings. Although the number of papers included in this review was limited, the selection process was completed with a systematic process to avoid selection bias. Future reviews may want to expand on the number of articles reviewed by using additional databases and including conference proceedings and open-access journals to obtain more up-to-date research trends. 6. Conclusions Although this review was not meant to be comprehensive, it provides important findings that can be useful for instructional designers and researchers. Four types of mobile apps for science learning emerged from the literature: placebased data collection tools, games/simulations, learning management systems, and productivity tools. There were a number of similar design features found across these apps, including technology-based scaffolding, location-aware functionality, visual/audio representations, digital knowledge-construction tools, digital knowledge-sharing mechanisms, and differentiated roles. In closely examining the effectiveness of these features, this review recommends that future research make use of newer, available technologies; develop additional strategies around using mobile apps for collaboration; and isolate the testing of specific app features. To assess the design of the mobile apps, studies measured a variety of student outcomes, ranging from scientific process skills to knowledge construction and synthesis; however, the most common measured outcome was students' basic scientific knowledge or conceptual understanding. There is a need for researchers to diversify their measures to include students' higher-level cognitive outcomes, cognitive load, and skill-based outcomes such as problem solving. Finally, more research is needed on how science mobile apps can be used with more varied science topics and diverse audiences. Using a grounded learning systems design analysis framework (Hannafin et al., 1997), this review found that many of the studies cited a specific theoretical foundation and applied this to the design of the mobile learning environment. However, researchers need to make more explicit connections between the instructional principles and the design features of their mobile app in order to better integrate theory with practice. This review also found some discrepancies between the underlying theoretical frameworks and the outcomes measured, making better alignment a necessity in future studies. 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