Advances in Mobile Learning Educational Research

Digital Game-Based Learning in Early Childhood and Primary Mathematics Education: A Systematic Review

2026-07-14T21:23:56+08:00

Digital Game-Based Learning (DGBL) has emerged as a promising approach for enhancing mathematics learning across educational contexts. Although previous reviews have examined the effectiveness of digital games in mathematics education, limited attention has been devoted specifically to early childhood and primary education. This systematic review synthesises empirical studies investigating the use of DGBL in mathematics education for children up to 12 years of age. Following PRISMA guidelines, studies published between 2006 and 2024 were identified through searches in Scopus, Web of Science, and Google Scholar. After applying predefined inclusion and exclusion criteria, 103 empirical studies were selected for analysis. The findings reveal a substantial increase in research activity after 2015, with Educational Technology journals being the primary publication venue. Quantitative research designs predominated, while tablets and personal computers were the most frequently used devices. Puzzle games represented the most common game genre. The majority of studies focused on Number and Operations, followed by Algebra, Geometry, and Measurement. Results indicate predominantly positive effects on students' mathematical achievement, particularly in Number and Operations and Geometry. Furthermore, digital games were associated with improvements in motivation, attitudes towards mathematics, and collaborative learning. The review highlights the educational potential of DGBL in mathematics and identifies directions for future research and practice.

Mobile Learning for Programming Education: A Case Study of SoloLearn and Self-Directed Learning Skills

2026-07-08T18:48:30+08:00

This study investigates how mobile learning supports self-directed learning (SDL) in programming education through a case study of SoloLearn. A cross-sectional survey of 708 undergraduate students at the University of Education, Winneba examined app usage, perceptions, and programming confidence, framed by Knowles’ (1975) SDL Theory and Fredricks et al.’s (2004) Student Engagement Framework. Partial least squares structural equation modeling (PLS-SEM) revealed a sequential pathway from self-management to motivation, monitoring, strategy use, and academic performance. Most participants (46.8%) reported increased confidence, while 38.4% reported improvements in real-world application skills, though challenges included limited advanced content, ad disruptions, and insufficient feedback. The findings suggest that SoloLearn effectively develops foundational SDL skills but requires adaptive features, project-based modules, and improved collaborative tools to support deeper learning. Results should be interpreted cautiously due to the single-institution, male-dominated sample. Overall, the study makes three contributions: it provides empirical evidence of a sequential SDL pathway from self-management to academic performance in mobile programming education; it demonstrates the value of combining objective grade data with self-report measures in mobile learning research; and it offers practical guidance for integrating mobile coding platforms into programming instruction across diverse educational contexts.

Offline-Priority Embodied AI Feedback in Rural Physical Education: Effects on Task-Specific Body-Awareness Expression and Standing Long Jump Performance

2026-07-03T11:48:20+08:00

In rural low-resource physical education settings, whether an offline-priority embodied AI feedback protocol is feasible and can improve students' task-specific expression of bodily sensations still requires empirical testing. This quasi-experimental study involved 72 first-year rural high school students (36 males and 36 females) randomly assigned to either the embodied AI group or the traditional AI feedback group. Both groups completed a 3-week standing long jump intervention (one 45--60 minute class per week, with 8 attempts per class). The experimental group used an offline-priority mobile learning ecosystem---teacher smartphone rapid key frame capture, data cable offline upload, local server AI processing, and iFlyTek voice input---to guide the "awareness--prediction--re-practice'' loop, while the control group received only traditional technical feedback. The teacher circulated between the two groups during instruction. Linear mixed-effects models and Bootstrap mediation analysis were employed to examine the effects. Results showed that standing long jump distance improved by approximately 13% in both groups (embodied AI group: +13.13%; traditional AI group: +13.11%), with a non-significant time × group interaction (p > 0.05). However, the embodied AI group demonstrated significantly higher task-specific body-awareness expression scores than the control group (Cohen's d = 3.239, p < 0.001). Mediation analysis indicated that task-specific body-awareness expression did not significantly mediate the relationship between group assignment and improvement in jump performance. The findings suggest that an offline-priority embodied AI feedback protocol is feasible in rural low-resource environments and effectively enhances students' ability to describe, monitor, and predict bodily sensations in the specific context of standing long jump practice. However, it did not produce superior short-term gains in standing long jump performance compared with traditional AI feedback. Because the embodied protocol necessarily involved more dialogue turns and reflection time, future studies should employ yoked designs to isolate the independent effects of feedback type and interaction dosage.

Artificial Intelligence in Education: Opportunities, Risks, and Pedagogical Implications for Learning and Assessment

2026-05-18T22:43:28+08:00

Generative artificial intelligence (AI) is changing teaching, learning, evaluation, and feedback methods in educational settings. An increasing body of research indicates that while AI-supported technologies improve efficiency, customisation, and access to learning resources, they may also reduce evidence of meaningful learning while simultaneously improving observable academic performance. Using interrelated pedagogical, cognitive, ethical, assessment, and policy viewpoints, this study critically investigates AI in education. The study provides an integrative understanding of AI integration in education by synthesizing literature from empirical investigations (n = 21), conceptual papers (n = 19), policy reports (n = 5), and one review study using a critical narrative review design. The results show that although AI improves feedback, student engagement, instructional support, and chances for adaptive learning, it also creates issues with cognitive dependence, academic integrity, algorithmic bias, data privacy, inequality, and assessment validity. The review's main conclusion is that there is a growing conflict between meaningful learning and visible academic achievement since AI-mediated outputs might not always demonstrate prolonged cognitive engagement or conceptual grasp. The study contends that pedagogical design, teacher involvement, AI literacy, institutional governance, and technological competence all play a role in the educational usefulness of AI. The paper emphasizes the necessity of learning and assessment systems that support responsible, egalitarian, and learner-centered AI integration in educational settings while preserving learner agency and making thinking visible.

A WeChat-Delivered Offline AI Toolchain for Hardware Simulation Teaching in Rural High Schools: An Exploratory Feasibility Pilot

2026-05-13T11:01:45+08:00

Rural high schools in low-resource environments face substantial barriers to AI-enhanced hardware simulation, including limited network bandwidth (e.g., 2G), low-specification devices (memory below 4 GB), and a lack of localized offline tools. This exploratory feasibility pilot study proposes and preliminarily evaluates a low-resource open-source AI simulation toolchain. The toolchain incorporates a MobileViT behaviour detection module and integrates it with open-source tools such as QEMU, Logisim-evolution, Tinkercad AR, MagicSchool.ai, and Blender to create a WeChat-delivered, offline AI-supported composite toolchain. The toolchain was delivered via WeChat to 25 students at a rural high school in central China over an 8-week A/B testing intervention. It was optimized for offline compatibility, localization training based on rural agricultural scenarios, and privacy protection through anonymous IDs. The study employed exploratory descriptive statistical methods. In this n = 25 exploratory feasibility pilot, descriptive statistics revealed positive descriptive trends in the experimental group for learning efficiency, test accuracy, and participation rate (task completion time reduced by approximately 30%, accuracy rate approximately +25%, participation rate approximately +28%). All statistical results are exploratory findings and require further validation with larger samples. Preliminary observations of the composite toolchain are directionally consistent with certain assumptions of Cognitive Load Theory (CLT) and Self-Determination Theory (SDT). This study provides an exploratory feasibility description of a WeChat-delivered, offline AI-supported toolchain for hardware simulation teaching in rural high schools. Rather than offering evidence of effectiveness, it identifies practical design considerations, implementation challenges, and preliminary descriptive trends that may inform future large-scale research in resource-constrained educational settings.

Undergraduate Students’ Perceptions of Generative Artificial Intelligence (Gen AI) in Academic Assignments

2026-04-27T10:44:35+08:00

The integration of generative artificial intelligence (Gen AI) into educational settings represents a transformative shift, reshaping conventional educational practices and students’ learning experiences. This study provides valuable insights for the effective educational implementation of Gen AI. The primary objective of this research is to examine how undergraduate students enrolled in educational technology programmes in Nepal perceive the effects of Gen AI on their academic assignments, as well as to identify the factors influencing students’ acceptance and adoption of Gen AI for these tasks. A quantitative research design was adopted, with data collected via an online survey from 174 undergraduate students pursuing a Bachelor in Technical Education in Information Technology in Nepal. Three core variables were measured in this study: the perceived impact of Gen AI, perceived ease of use, and the determinants of Gen AI acceptance and adoption. Approximately 64% of students utilised AI tools on a daily basis when completing their assessment tasks. Respondents demonstrated a relatively positive perception toward the utilisation of Gen AI in assignments (M = 3.82, SD = 0.44). Students held strong beliefs regarding Gen AI’s influence on their assignment completion (M = 3.82, SD = 0.32). They identified instructors’ integration of AI in teaching as a key factor motivating their own use of Gen AI tools. Furthermore, students predominantly relied on mobile applications to access generative AI, highlighting the relevance of portable devices within mobile learning environments. In addition, participants perceived such technologies as user-friendly for completing academic assignments. The findings of this study hold practical implications for educators, institutional policymakers, university administrators, and students interested in the transformative potential of generative artificial intelligence (Gen AI) within educational contexts.

Beyond Adoption: Investigating Long-Term Digital Library Service Usage in Higher Learning Institutions

2026-04-20T15:45:57+08:00

The growth of wireless technologies and ubiquitous mobile devices has transformed the way library resources and services are delivered to users. Most libraries have adopted mobile library applications (MLA) to improve their service delivery. Despite such widespread adoption, little attention has been paid to the long-term viability of MLAs for service provision. Accordingly, this study investigates mobile library application continuance usage intention among users of Higher Learning Institutions (HLIs) in Tanzania. This study integrates the Expectation Confirmation Model, Technology Acceptance Model, and Information System Success Model to explore library users’ continuance intention toward MLAs. Furthermore, the integrated framework is extended by incorporating perceived value and application accessibility, while the moderating role of habit on continuance usage behaviour is also examined. A random sampling method was adopted to collect 361 valid and complete responses from libraries across Tanzanian HLIs for data analysis. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to test the proposed hypothetical relationships. The results reveal that perceived usefulness, user satisfaction, and habit exert significant positive effects on users’ continuance usage of MLAs in HLIs. Moreover, service quality and system quality significantly affect perceived usefulness, and perceived usefulness in turn significantly influences perceived value. In addition, confirmation is found to positively affect both perceived usefulness and user satisfaction with MLA usage, whereas application accessibility significantly impacts perceived ease of use. This study yields theoretical contributions and practical implications, which facilitate subsequent scholarly research on MLAs, and support policymakers and service providers in formulating sustainable strategies for digital library services within HLIs.

Exploring 360° Camera Deployment in a STEM Rotation-Station Laboratory: Pre-service Teachers’ Perceptions

2026-03-19T19:25:15+08:00

360° cameras allow for comprehensive spatial coverage in classroom settings; however, the use of these devices in STEM teacher education labs has not been adequately investigated. This exploratory study (N = 16 pre-service teachers) aimed to investigate the perceptions of pre-service teachers on the use of a 360° camera installed in a university STEM rotation-station laboratory equipped with Arduino, BBC micro:bit, Makey-Makey, and Raspberry Pi devices. A researcher-designed questionnaire was used to collect data on pre-service teachers’ perceptions on the three aspects of the 360° camera’s use in a STEM teacher education setting: (a) the observer effect, (b) the value of the 360° camera for teacher improvement, and (c) the potential of interactive hotspot annotations on the video feed. The results revealed minimal perceived reactivity: 60.0% of participants reported no/minimal effect on themselves, and 73.3% perceived no/minimal effect on the instructor. The value of the 360° camera for improvement was highly endorsed by the pre-service teachers; likewise, the potential of interactive hotspot annotations on the video feed for prospective teacher practice was highly appreciated by the pre-service teachers. Three themes were found to be relevant to the pre-service teacher perceptions on the use of the 360° camera in a STEM teacher education setting: complete spatial coverage, potential to monitor engagement, and potential for reflective improvement. Data on course evaluation and self-assessed knowledge confirmed the authentic and engaging nature of the setting for pre-service teacher learning. The findings should be viewed with caution as they are based on self-report data collected from a small sample of convenience; therefore, more research should be conducted to corroborate these findings using more objective methods and larger and more diverse samples.

Exploring Students’ Perceptions of Generative AI: Benefits, Challenges, and Academic Ethics

2026-03-10T14:45:32+08:00

The emergence of Generative Artificial Intelligence (GAI) has transformed how students interact with technology in academic contexts. This study aims to explore students' perceptions of the benefits, challenges, and academic integrity related to GAI usage. The research was conducted through a survey involving 71 students from the Library and Information Science Program who have used GAI in their academic activities. Data was collected using a 5-point Likert scale and analyzed descriptively using SPSS software. The findings reveal that students perceive GAI as a tool that facilitates academic tasks, improves time efficiency, and enhances language and critical thinking skills. However, they also identify various challenges, including privacy risks, information reliability, and potential plagiarism. Despite these concerns, awareness of academic integrity remains high, with students emphasizing the importance of honesty and originality in utilizing this technology. These findings provide valuable insights for educational institutions in designing policies and learning strategies that maximize the benefits of GAI while mitigating its risks. This study is expected to serve as a foundation for developing digital literacy and academic policies that are more adaptive to technological advancements. Furthermore, the accessibility of GAI through mobile devices opens opportunities for the development of learning strategies by shifting the learning paradigm from conventional classrooms toward ubiquitous learning, enabling students to learn flexibly without the constraints of time and place.

Students' Pre-Instruction Programming Perceptions in Upper-Secondary School: Findings from a Diagnostic Pilot

2026-02-28T17:08:53+08:00

Pre-instructional diagnostic assessments allows educators to target specific novice misconceptions learners bring to a subject. This data allows them to adjust their initial lesson plans to address these common errors immediately. This pilot study reports on a pre-instruction diagnostic administered to Grade 11 students in one upper-secondary school. The instrument, composed of multiple-choice and True/False items aligned with five conceptual clusters (definition of a program, language recognition, variables and data, basic conditionals, and elementary loop semantics), was designed to reveal common novice difficulties documented in the literature. Analyses of students' responses indicated partial familiarity with simple control constructs but persistent weaknesses in foundational areas, including distinguishing a program from an algorithm, understanding variables as memory locations, and recognizing the role of guard change in loop termination. A consistent format effect favored recognition-based True/False items over multiple-choice discrimination, suggesting that early instruction should bridge from recognition to explanation and short code construction. Although limited by its single-site scope, the pilot provides a practical baseline for refining diagnostic tools and informing initial instructional sequencing in upper-secondary programming.