Social Mobile Learning - Case studies of a new learning methodology in Indonesia

Aderina Febriana Sajogo
Lecturer, Development and Learning Center, Universitas Prasetiya Mulya
Indonesia

Kelly Rosalin
Lecturer, Chinese Department, Binus University
Indonesia

Ardo Ryan Dwitanto
Assistant Professor, IPMI International Business School
Indonesia

Daniel Shen
Founder, Soqqle Pte ltd
Singapore

Abstract

Social Learning is of mounting interest to educators due to the growing adoption of mobile learning and social media applications. Recently, the growth of newer social media apps, like TikTok and Instagram, has created a new range of technological usages, such as student-generated videos on mobile devices. However, little research is available on up to date 21st-century social tools and how they can be adapted into learning. This paper expands on blended learning by assimilating social learning theory with mobile learning as a new form of learning methodology. The potential to create an environment in which there is a positive environment for learning and an increase in interest and motivation is high. This study uses a non-experimental test design where the authors adapt the use of a new social learning technology mobile application in three Indonesian universities during the semester of February to June 2020.  The authors creatively planned and designed a lesson curriculum, in which they embedded Student-generated video tasks. An anonymous online survey was distributed to capture student opinions. The authors used t-tests and the analysis of variance in order to measure the tests. The results are promising. All participating universities record positive learning satisfaction from students for this technology-supported social learning methodology. Students also appear to be receptive to peer to peer learning. Additionally, social learning technology with learning analytics may boost teaching efficiency.

Keywords: social learning, blended learning, mobile learning, student-generated videos, new learning methodology, social learning technology

Introduction

Modern technology undoubtedly has, over the last decade, vastly changed our teaching and learning processes. For example, interactive whiteboards have progressively replaced old-fashioned chalkboards. The way students communicate with fellow peers has promptly changed to mobile communication. Educational approaches have also experienced technological adaptations. Recent methods, like blended learning, have been aptly described as ‘the new normal’ (Dziuban et al., 2018). However, as technology evolves naturally, academic expectations from students have also changed. The significant rise of social networks has invariably led to new social behaviors for current student demographics. Social platforms like Facebook, Instagram, and TikTok have been popular around the modern world. Researchers study these platforms extensively (Yang et al., 2011; Wang, 2020; Carpenter et al., 2020). However, these social tools are not often designed for academic education. They are purportedly often not scalable nor sustainable for consistent use in learning. In this paper, we study intensely in an alternative learning methodology of using student-generated videos as a part of assessments, enabled by new educational technology.

Blended learning

Rasheed et al., (2020) considers blended learning to be one of the most effective and popular methodologies today. Blended learning is defined as an ideal combination of classroom learning with the internet (Bryan & Volchenkova, 2016). The potential benefits of this are many. They inevitably make classroom time more efficient, help drive creativity, and interests of students (Susan & Chris, 2015). For example, in an experimental study carried out (Schechter et al., 2015), results show that blended learning can positively enhance the reading skills of learners from low socioeconomic circumstances. Critical gaps are also reflected. Online-based instructions can also cause technological anxiety, complexity, and illiteracy. Another study (Broadbent, 2017) reported that the online component lacks the ability to provide big picture challenges of teaching and studying adequately. The seamless integration of teaching-learning processes with technological tools (Becker et al., 2017), is also critical. This paper will be conducted with a variation of blended learning – social mobile learning.  

Social Learning. Since the introduction of the Social Cognitive Theory (SCT) (Bandura, 1986), researchers have carefully reviewed the different impacts on society. Social Cognitive Theory (SCT) drives motivation (Schunk et al., 2019) and self-efficacy (Compeau & Higgins, 1995). Prior studies  also suggest the implementation of SCT-related attributes enhances critical thinking (Hamid et al., 2015) and collaboration skills (Borokhovski et al., 2016). It can also help to adequately enforce knowledge management (Liao et al., 2015). Understanding student demographics is crucial to planning for SCT's success.

Currently, student demographics often manage social connections through mainstream social media applications like Facebook, and TikTok. One study shows the utilitarian nature of social media in Indonesia (Makri et al., 2019). It can also increase students’ personal perception of their proficiency (Anwas et al., 2020). However, the depth of utilitarian usage of social media seems unclear (Ernst et al., 2013). For example, Paliktzoglou and Suhonen (2014) highlight skeptical attitudes from students towards using Facebook for formal learning. Personal attitude continues to be a significant factor for success (Sturner et al., 2017). One typical behavior of mainstream social media apps like Facebook and TikTok is its focus on mobile adoption. We look deeper into its possible application in Mobile Learning.

Mobile Learning. The interest in efficiently utilizing mobile technologies like smartphones, tablets, and other portable devices in the teaching-learning process is increasing. In Indonesia, 4G coverage has increased from 60% (2014) to 90% (2018), and mobile ownership has reached two-thirds (GSMA, 2019). However, there are reported limitations in utilizing mobile devices for learning. These include potential distractions due to the multi-purpose nature of mobile applications (Sana et al., 2013). The overuse of mobile devices can lead to cyber-procrastination (Flanigan,¬ & Kiewra, 2018). Cultural barriers also appear to represent a factor (Ariffin, 2011). Lastly, if used for learning, the setting of rubrics and clear instructions are also needed (Shen, 2020). One prevalent method social media applications employ today is user-generated content. For learning, a potential adoption of this is recognizably student-generated videos.

Student Generated Videos. A study suggests that user-generated videos encourage more favorable attitudes towards content than if shared by authority owners (Diwanji & Cortese, 2020). This study will attempt to extend that understanding to determine if student-generated videos could enhance student attitudes towards teacher content.  A study by Wang (2020) on the social media application, TikTok, suggests that short-form user-generated videos could instill social presence, humor, and increase the user’s intent to voluntarily adopt. Students could create and manage such a style of videos in order to facilitate learning. They could indeed provide active-learning environments and encourage principles of constructionism (Campbel et al., 2020). Another study on Tiktok (Omar & Dequan 2020) suggests that such social usage methods could affect user motivation. However, the authors were unable to find research on how the blending of such videos into regular education curriculum at scale, can be done.

Social Mobile Learning – a new learning methodology for blended learning

Based on the preceding discussions, mobile learning, and social media (using student-generated videos), if integrated for education, invariably produces an alternative type of blended learning.  Seamlessly blending these key factors develops a conducive learning environment naturally. It additionally provides a beneficial impact on student learning satisfaction. For example, students can easily add constructive comments on class discussions at any time and place (mobile-learning). Mobile videos can be recorded directly and efficiently to complete learning tasks (student-generated videos). Students can also examine task content from peers easily (social-learning) and exchange ideas. An efficient and successful execution increases potential learning interactions.

Social Learning Technology. Implementing student-generated videos for learning on mobile (social mobile learning) could indeed be done with mainstream applications today. However, it will be necessary to critically discuss the efficiency requirements for educators to consistently and sustainably conduct this methodology. As described before, a more significant volume of student interactions is required before students can undoubtedly benefit from this methodology. Therefore, technological infrastructure needs to support some level of scale for social mobile learning to be successful. For example, if students were asked to upload 100 videos a week, educators would reasonably demand a convenient way to examine the content and give feedback. A look into some Learning Management Systems reflects technical limitations in handling the size and bandwidth of videos. User experiences of these systems are also often not optimized for more substantial volumes of video recordings and pictures. Existing social media apps also appear to be unsuitable for students' academic objectives (Davies & Sant, 2014). Additionally, the authors were unable to reliably find any ideal platforms that could adequately provide learning analytics effectively to support critical evaluation.

Experiment Design

The alternative learning methodology proposed in this paper was embedded in suitable activities in the curriculum (Table 2). For Finance, the topic of public health to the economy was used. For language, numerous formal and informal topics related to the lesson objectives were used. Tasks related to lesson objectives were used, designed in a creative manner that required students to research and present material in a video (and sometimes picture) format. Students used 2 to 5-minute videos to demonstrate their understanding of topics.

Technology Requirements. Considering the hypotheses of this study, the required technology would need to be suitable for student demographics, support a substantial number of videos, and encourage group and social learning. The technology would also need to be able to provide learning analytics to help educators efficiently manage a potential increase in content volume. The authors chose the Soqqle application for the experiment. The features of the mobile app are shown in Table 3, below, and also describes how the features are relevant to the experiment design of this study.

Measurement Method & Tool.  Participants in three universities (total of 98 students) would complete tasks designed with the new learning methodology for a period of 12 weeks. To evaluate the effectiveness of the proposed research model, an online opinion survey was distributed to students in the last week of classes (week of 29 June 2020). For each hypothesis question in the research model, a corresponding survey question was used (Appendix Table 08). Participants answer each question with a Likert scale of 1-5. In addition, if students answered a score below 3, the survey automatically provided a set of secondary questions for students to share why they did provide favorable feedback (Appendix Table 9). Analysis of the results were split amongst the three universities. This was because each university had a different topic (Chinese language, English language, and Finance. The measurement methods are described in Table 4 below Participants in three universities (total of 98 students) would complete tasks carefully designed with the alternative learning methodology for a period of 12 weeks.

To evaluate the potential effectiveness of the proposed research model, an anonymous online opinion survey was distributed to students in the last week of classes (week of 29 June 2020). For each hypothesis in the research model, a corresponding survey question was given (Appendix Table 08). Participants answer each question with a Likert scale of 1-5. In addition, if students answered a score below 3, the survey automatically would provide a follow-up set of secondary questions for students. The secondary questions describe why they did not submit favorable feedback (Appendix Table 9). Comprehensive analysis of the results was segregated amongst the three universities. This was due to each participating university conducting experiments with a different topic (Chinese language, English language, and Finance). The measurement methods are described in Table 4 below.

Findings

Data Collection and Results

The results were obtained/collected for the three universities and analysed separately below. Binus University (n=9) received a total of 98 survey responses (df =98). For Prasetiya Mulya University (n=60) received a total of 27 survey responses (df=298). IPMI Business School (n=29) received a total of 29 survey responses (df=318). A reliability analysis of the survey results for the participating universities show a good Cronbach result (0.917). Findings are also summarized on the table below. (Appendix Table 10).

Binus University (Chinese Language).  Students (n=9) from Binus University produced 39 posts, contributed 51 comments, and generated 39 likes. The mean scores on the attributes in the survey suggest good indicators of the perception of students on the learning method (M = 3.657, SD = 1.061). A Shapiro-Wilk test on the responses showed a significant departure from normality (W = 0.865 , p < .001). This is suspected to be due to the smaller size of participants (n=9). Therefore, an additional Wilcoxon test was conducted on the responses (Z = 1575 , p < .001, d = -0.364 ), with a small effect size of -0.364. The parametric version (student’s) t-test suggests positive feedback (-t(98) = 6.157, p  <.001, d = 0.619). The ANOVA showed no significant mean difference between student perceptions of all hypothesis questions. (F (2,96) = 1.727, p = .183). This suggests that analysis categories of the study (mobile learning, social learning, individual skill) were of equal importance to student learning satifaction.

Students report that the new method was useful for them because of the ability to pace their recordings to complete the tasks. One such activity was for students to record a 5-minute video to complete an assignment. Students recorded cooking instructions and presented subtitles in Chinese language (Photo 1). This method of video assessments was a unique alternative to have text in subtitles, as opposed to plain text essays. The learning experience can now be made enjoyable. Context is also possible in this methodology. Students captured activities in their immediate surroundings and described them in Chinese language (Photo 2). Students also share detailed descriptions in captions of the posts. Lastly, to stimulate the use of language in day to day activity, students were required to add comments on each other’s videos. The students use what they had learned to create relevant sentence structures (Photo 3).

Prasetiya University (English Language). Students (n=60) in Prasetiya Mulya produced 170 posts, contributed 45 comments, and generated 13 likes. The median scores on the attributes in the survey were good indicators of the perception of students on the learning method (M = 4.0, SD = 1.070). The responses suggest an above average learning satisfaction to the new style of learning: t(296) = 11.439, p  <.001, d = 0.664.  The ANOVA shows no mean difference between student perceptions of all hypothesis questions.  (F (2,294) = .08, p = .923). This suggests that all analysis categories of the study (mobile learning, social learning, and individual skill) were of equal importance.

Students report that completing tasks based on relatable content makes the learning process more enjoyable. Students also enjoyed uploading pictures from their mobile phones. These photos may represent topics of interest to the students. Students also upload videos and describe them in English (Photo 4, Photo 5).  Students also upload existing photos from their phones and describe it in English (Photo 6). Overall, this learning style makes learning contextual for students. Besides identifying common student interests, it increases social engagement between students. The teacher could also view keyword hashtags available on a separate website dashboard.

IPMI Business School (Finance). Students (n=29) in IPMI Business School produced 97 posts, 195 comments, and 379 likes. The median scores on the attributes in the survey reported good indicators of the perception of students on the learning method (M = 4.1, SD = 0.829). The responses suggest an above average satisfaction to the new style of learning: t (296) = 23.693, p  <.001, d = 1.327.  The ANOVA showed that there is no significant mean difference between student perceptions for all hypothesis questions: (F (2,316) = .287, p = .751). This suggests that all analysis categories of the study (mobile learning, social learning, and individual skill) were of equal importance to student satisfaction.

The scores from student participants evidence positive responses to the new learning methodology. Students report that it is helpful to learn from others on the Soqqle app. Students can read comments from other students. The high number of comments seen in this scenario shows a high level of engagement.  The author notes that this method is useful for topics like Finance, where student research and discussion is often needed. One example is a 2-minute presentation of the impact of COVID to the economy (Photo 7, Photo 8). Students also responded and gave peer to peer feedback (Photo 9). To do this, students use the comments feature on the video posts.

Responses From Surveys With A Score Below Three. As the online survey setup included requesting a secondary response if the student keyed in a score below 3, the authors were able to study the reasons for a low rating (Table 5). The authors categorized the responses into common themes. For example, ‘It seems awkward and out of place’ is grouped with ‘Not my style’. ‘It was unclear what to do’ was grouped with “It was confusing’.

The analysis of the scores where students recorded a response of below three was studied. 36% were due to ‘not my style’ choices, while 22% were due to confusion, and 20% to a lack of incentives. Comparing the numbers across the wider population, ‘not my style’ choices make up 80% of the all students  (4 of 5 students). As the survey was anonymous, it was impossible to link the responses to the actual students. A possible reason may be due to the new method of learning. It is also possibly due to a new customized mobile application.

Social Learning Technology. Lessons were able to be conducted consistently with the new social learning technology (Table 6), with the following features. Students can record videos and capture pictures with the new mobile application. Educators review the outcome on a consolidated website dashboard (Photo 10). Comments are available on every post, so personalized feedback is possible.  Teachers can also change the view to show content grouped by student names.

Discussion

The responses from the participants show a meaningful result.  Variance tests were also not significant. This may have multiple implications. Deeper causal relationships will be required to evaluate on the correlations of a potential social learning model. However the consistency of results of the questionaire shows a potential learning gains . The table (Appendix Table 10) shows a summarized questionaire results during this study.

Based on the questionaire results, we propose the following hypotheses:

  1. Technical features to support group based discussions, and student-generated content (videos eg.) lead to increased satisfaction of educational technology adoption
  2. Group learning and social learning lead to increased satisfaction of educational technology adoption
  3. Personal skill with a instruction, and rubrics lead to increased satisfaction of educational technology adoption

Finally, we place the differences between topics (Language and Finance) in a table (Table 7). Results suggest that mobile social learning, using student-generated videos and comments as discussions, potentially boosts student learning. This may be due to like a perceived increase in group learning and openness in communication. The increase in group learning was most evident in the Finance scenario, where students contributed a fair number of comments. The nature of the module may have resulted in more opportunities for discussions. Students also need to identify with a clear utility purpose of the learning methodology. Without direct instructions and rubrics, student participation is likely to be decreased. The survey scores from students who recorded below a score of 3 reinforced this understanding.

The authors also observe the benefits of new media formats, like pictures and videos. These formats are more likely to capture the attention of students. It was also more convenient for educators to use a mobile app to deliver feedback to students with the comments feature. Lastly, a consolidated view as a form of learning analytics is also needed. This view can help to link tasks for evaluating student competencies. Without the mentioned components, the methodology would not scale to more considerable class sizes and be sustainable for a long time. This study established an alternative learning methodology for language and finance classes, and a modern tool for social mobile learning to affect learners’ performance.

Discussion

The adoption of mobile and social media in Asia is significant. Although previous research with social media for learning exist, they may not be perceived as relevant today. Many used tools that students may see as outdated today. Student demographics also evolve regularly, transforming the way they derive motivation and interest in education. This study's primary goal is to create and experiment with an experimental social learning methodology built around student-generated videos. The methodology would involve the design of activities familiar to 21st-century learners, with social media interactions. Analysis categories in the research questions focus on social learning, mobile learning, and personal skill. The study also discusses the result of using a new type of social learning technology. To receive feedback from students, the authors conducted an online survey with 11 items in the last week of classes. The statistical tests in this study employ T-tests and analysis of variance (ANOVA).

The results highlighted that social mobile learning may have a positive impact to learning. The highest noted mean score is 4.1 (out of a scale of 5). All hypotheses additionally have a relatively close mean. They could be because the factors in the hypotheses are equally critical for application, or there is a relationship not included in this study. The authors observe that social learning analytics from students could help teachers personalize teaching. The affordance of modern technology and student demographics makes this possible. The authors additionally take into account that comparison (or controls) groups were not part of the experiment design. This is because the study is designed as a preliminary investigation to identify initial student adoption feedback. The study currently recommends subsequent experiments to carefully explore further factors in the alternative learning methodology. More social learning tasks to identify opportunities to build a sustainable method should equally be considered.

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Daniel Shen

Daniel Shen