Exploring the Challenges of Supporting Collaborative Mobile Learning

Mobile technology opens up opportunities for collaborative learning in otherwise remote contexts outside the classroom. A successful realization of these opportunities relies, however, on mobile learning activities providing adequate collaboration structures. This article presents an empirical study aimed at examining the role played by mobile devices, teachers and task structures as a means for collaborative learning in geometry. The study focused on the analysis of the nature of collaboration that unfolded when students measured areas outdoors in the field. The analysis of the mobile learning activity was conducted from an Activity theory perspective. The findings obtained indicate that the collaboration observed may be impaired if: 1) the functionalities needed for collaborative problem-solving are asymmetrically distributed on a number of mobile devices; 2) task-related information is not accessible to all learners; 3) the task structure is not sufficiently complex; 4) teacher scaffolding is too readily available; and 5) necessary collaborative skills are not developed.

Download Full-text

An overview of mobile assisted language learning: From content delivery to supported collaboration and interaction

ReCALL ◽

10.1017/s0958344008000335 ◽

2008 ◽

Vol 20 (3) ◽

pp. 271-289 ◽

Cited By ~ 303

Author(s):

Agnes Kukulska-Hulme ◽

Lesley Shield

Keyword(s):

Collaborative Learning ◽

Language Learning ◽

Mobile Devices ◽

Mobile Learning ◽

Rapid Evolution ◽

Social Contact ◽

Learning Needs ◽

Social Contacts ◽

Personal Learning ◽

Mobile Assisted Language Learning

AbstractMobile learning is undergoing rapid evolution. While early generations of mobile learning tended to propose activities that were carefully crafted by educators and technologists, learners are increasingly motivated by their personal learning needs, including those arising from greater mobility and frequent travel. At the same time, it is often argued that mobile devices are particularly suited to supporting social contacts and collaborative learning - claims that have obvious relevance for language learning. A review of publications reporting mobile-assisted language learning (MALL) was undertaken to discover how far mobile devices are being used to support social contact and collaborative learning. In particular, we were interested in speaking and listening practice and in the possibilities for both synchronous and asynchronous interaction in the context of online and distance learning. We reflect on how mobile language learning has developed to date and suggest directions for the future.

Download Full-text

Exploring the Role of Mobile Learning in Global Education

Research Anthology on Developing Effective Online Learning Courses ◽

10.4018/978-1-7998-8047-9.ch064 ◽

2021 ◽

pp. 1303-1315

Author(s):

Kijpokin Kasemsap

Keyword(s):

Collaborative Learning ◽

Learning Environment ◽

Blended Learning ◽

Mobile Devices ◽

Mobile Learning ◽

Global Education ◽

Mobile Technologies ◽

Easy Access ◽

Current Trends

This chapter describes the current trends of mobile devices in education, the applications of mobile technologies in learning, the overview of Mobile Learning (m-learning), and the importance of m-learning in global education. M-learning encourages both blended learning and collaborative learning, thus allowing the learners at different locations to get in touch with their peers or others teams to discuss and learn. The m-learning environment is about access to content, peers, experts, portfolio artifacts, credible sources, and previous thinking on relevant topics. Given the convenience of m-learning, there is less time spent getting trained, and the overall costs are lowered as a results. With m-learning, learners are able to learn in their own style at their own pace. M-learning provides easy access to the learning at any place and any time, which is more convenient to the learners.

Download Full-text

Exploring the Role of Mobile Learning in Global Education

Handbook of Research on Instructional Systems and Educational Technology - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-2399-4.ch027 ◽

2017 ◽

pp. 313-325

Author(s):

Kijpokin Kasemsap

Keyword(s):

Collaborative Learning ◽

Learning Environment ◽

Blended Learning ◽

Mobile Devices ◽

Mobile Learning ◽

Global Education ◽

Mobile Technologies ◽

Easy Access ◽

Current Trends

This chapter describes the current trends of mobile devices in education, the applications of mobile technologies in learning, the overview of Mobile Learning (m-learning), and the importance of m-learning in global education. M-learning encourages both blended learning and collaborative learning, thus allowing the learners at different locations to get in touch with their peers or others teams to discuss and learn. The m-learning environment is about access to content, peers, experts, portfolio artifacts, credible sources, and previous thinking on relevant topics. Given the convenience of m-learning, there is less time spent getting trained, and the overall costs are lowered as a results. With m-learning, learners are able to learn in their own style at their own pace. M-learning provides easy access to the learning at any place and any time, which is more convenient to the learners.

Download Full-text

Cooperative Mobile Learning for the Investigation of Natural Science Courses in Elementary Schools

Sustainability ◽

10.3390/su12166606 ◽

2020 ◽

Vol 12 (16) ◽

pp. 6606 ◽

Cited By ~ 1

Author(s):

Po-Sen Huang ◽

Po-Sheng Chiu ◽

Yueh-Min Huang ◽

Hua-Xu Zhong ◽

Chin-Feng Lai

Keyword(s):

Collaborative Learning ◽

Cooperative Learning ◽

Learning Outcomes ◽

Mobile Devices ◽

Mobile Learning ◽

Natural Science ◽

Learning Approach ◽

Google Docs ◽

Personal Learning ◽

The Impact

The rapid development of technologies such as tablet PCs and 4G/5G networks has further enhanced the benefits of mobile learning. Although mobile devices are convenient and provide a variety of learning benefits, they are unable to improve students’ learning outcomes without an appropriate learning strategy. Furthermore, little research has been conducted to examine the effects of using collaborative learning on mobile devices. This study proposed a cooperative learning framework using Google Docs to explore the learning outcomes of students of natural science in an elementary curriculum. The study was of a quasi-experimental design with an experimental group (cooperative learning) and a control group (personal learning). The results show that a cooperative learning approach using Google Docs improved learning outcomes, teaching interest, and understanding of campus plants, and reduced cognitive load. One conclusion of the study is that the collaborative learning approach associated with mobile learning is more effective than personal learning. In addition, this paper also provides brief recommendations to expand on the study’s limitations. Future work should investigate the impact of collaborative learning on different environments for mobile learning.

Download Full-text

Collaborative Mobile-Learning Architecture Based on Mobile Agents

Electronics ◽

10.3390/electronics9010162 ◽

2020 ◽

Vol 9 (1) ◽

pp. 162 ◽

Cited By ~ 1

Author(s):

Samer Atawneh ◽

Mousa Al-Akhras ◽

Iman AlMomani ◽

Anas Liswi ◽

Mohammed Alawairdhi

Keyword(s):

Collaborative Learning ◽

Mobile Learning ◽

Learning Process ◽

Mobile Agents ◽

Information Transfer ◽

Learning Activity ◽

Main Role ◽

Social Agent ◽

Proposed Model ◽

The Social

The connection between collaborative learning and the new mobile technology has become tighter. Mobile learning enhances collaborative learning as learners can access information and learning materials from anywhere and at any time. However, supporting efficient mobile learning in education is a critical challenge. In addition, incorporating technological and educational components becomes a new, complex dimension. In this paper, an efficient collaborative mobile-learning architecture based on mobile agents is proposed to enhance learning activity and to allow teachers and students to collaborate in knowledge and information transfer. A mobile agent can control its own actions, is able to communicate with other agents, and adapts in accordance with previous experience. The proposed model consists of four components: the learner agent, the teacher agent, the device agent and the social agent. The social agent plays the main role in the collaborative tasks since it is responsible for evaluating the collaborative interactions among different learners. Additionally, it offers an evaluation indicator for the learners’ collaboration and supplies the teacher with learner’s collaboration reports. The proposed model is evaluated by introducing a collaborative mobile-learning case study applied to two full classes of undergraduate students. To conduct the model experiments, students were asked to complete a questionnaire after they used the proposed model. The questionnaire results statistically revealed that the proposed architecture is easy to use and access, well-organized, convenient, and facilitates the learning process. The students thought the proposed m-learning application should complement rather than replace the traditional lectures. Moreover, the experimental results show that the proposed collaborative mobile learning model enhances the learner’s skills in problem solving, increases the learner’s knowledge in comparison with individual learning, and social agent encourages learners for more participation in the learning tasks. Based on the experiments conducted, the authors found that the proposed model can improve the quality of the learning process by assessing learners’ and groups’ collaboration, and it can help teachers make learners improve how they work in groups. This also provides various ways of assessing learners abilities and skills in groups. It is also possible to integrate the collaborative e-learning with the proposed collaborative m-learning.

Download Full-text

Deep neural networks for collaborative learning analytics: Evaluating team collaborations using student gaze point prediction

Australasian Journal of Educational Technology ◽

10.14742/ajet.6436 ◽

2020 ◽

Vol 36 (6) ◽

pp. 53-71

Author(s):

Roghayeh Barmaki ◽

Zhang Guo

Keyword(s):

Collaborative Learning ◽

Student Learning ◽

Learning Outcomes ◽

Learning Analytics ◽

Student Learning Outcomes ◽

Collaborative Problem Solving ◽

Assessment Process ◽

Learning Activity ◽

Cost Constraints ◽

Significant Difference

Automatic assessment and evaluation of team performance during collaborative tasks is key to the research on learning analytics and computer-supported cooperative work. There is growing interest in the use of gaze-oriented cues for evaluating the collaboration and cooperativeness of teams. However, collecting gaze data using eye-trackers is not always feasible due to time and cost constraints. In this paper, we introduce an automated team assessment tool based on gaze points and joint visual attention (JVA) information drawn from computer vision solutions. We evaluated team collaborations in an undergraduate anatomy learning activity (N = 60, 30 teams) as a test user study. The results indicate that higher JVA was positively associated with student learning outcomes (r(30) = 0.50, p < 0.005). Moreover, teams who participated in two experimental groups and used interactive 3D anatomy models, had higher JVA (F(1,28) = 6.65, p < 0.05) and better knowledge retention (F(1,28) = 7.56, p < 0.05) than those in the control group. Also, no significant difference was observed based on JVA for different gender compositions of teams. The findings from this work have implications in learning sciences and collaborative computing by providing a novel joint attention-based measure to objectively evaluate team collaboration dynamics. Implications for practice or policy: Student learning outcomes can be improved by receiving constructive feedback about team performances using our gaze-based collaborative learning method. Underrepresented and underserved minorities of science, technology, engineering and mathematics disciplines can be engaged in more collaborative problem-solving and team-based learning activities since our method offers a broader reach by automating collaboration assessment process. Course leaders can assess the quality of attention and engagement among students and can monitor or assist larger numbers of students simultaneously.

Download Full-text

ACCOMMODATING MOBILE LEARNING IN COLLEGE PROGRAMS

Online Learning ◽

10.24059/olj.v17i1.314 ◽

2013 ◽

Vol 17 (1) ◽

Cited By ~ 2

Author(s):

Jay Alden

Keyword(s):

Mobile Devices ◽

Mobile Learning ◽

Text Messaging ◽

Teaching And Learning ◽

Discussion Board ◽

Academic Programs ◽

Higher Learning ◽

College Programs ◽

Near Future ◽

Selected Functions

Mobile devices and applications are expected to have a significant impact on teaching and learning in the near future. Yet colleges and universities are currently facing severe budget constraints and discretionary funding is restricted for new initiatives. The question addressed in this paper is: “What strategy should an institution of higher learning with limited resources use in adapting the capabilities of mobile devices to benefit its academic programs?” To help answer this question, students were surveyed to identify their perceptions on the importance of a selected set of mobile learning functions, their experience with using those functions, their recommendation for a mobile learning adoption strategy, and information on the particular mobile devices they possess. The recommended strategy was “pick and choose special capabilities to develop” with the selected functions being (1) Receive alerts and reminders about assignments and appointments concerning the course being taken; (2) Communicate individually with faculty, an advisor, or other students using voice, email, or text messaging; (3) Post or reply to items in a poll, discussion board, or other application; and (4) Download and review lesson materials from a course being taken. Other recommendations included techniques for faculty and student support services as well as institutional policies for limiting models of mobile devices for use in courses, making online courseware for laptops and desktops the same as mobile learning courseware, and making the opportunity for mobile learning optional.

Download Full-text