Exploring Students Engagement Towards the Learning Management System (LMS) Using Learning Analytics

Ismail, Shahrul Nizam; Hamid, Sharifah Bee Binti O.A Abd; Ahmad, Muneer; Alaboudi, Abdulellah A.; Jhanjhi, N. Z.

doi:10.32604/csse.2021.015261

Cited by 26 publications

(11 citation statements)

References 46 publications

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“…Therefore, one potential revision to the current design of the collaborative tutoring system is to encourage Tutors to engage in more chat, especially facilitative chat. For example, the system could be redesigned to increase trust in the (anonymous) chat partner, as prior work showed associations between trust and engagement in learning management systems [25]. An alternative solution could be to redesign the ITS to help Tutors grade the steps of the Solver more automatically once Tutors have demonstrated mastery of the relevant steps to free up more resources to tutor through chat.…”

Section: Discussionmentioning

confidence: 99%

Combining Dialog Acts and Skill Modeling: What Chat Interactions Enhance Learning Rates During AI-Supported Peer Tutoring?

Borchers,

Yang,

Lin

et al. 2024

Preprint

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Peer tutoring can improve learning by prompting learners to reflect. To assess whether peer interactions are conducive to learning and provide peer tutoring support accordingly, what tutorial dialog types relate to student learning most? Advancements in collaborative learning analytics allow for merging machine learning-based dialog act classification with cognitive modeling of fine-grained learning processes during problem-solving to illuminate this question. We estimate how much peer-tutored students improve in a collaborative tutoring system for linear equation-solving in K-12 mathematics in relationship to the peer dialog types they engage in. This work establishes a reliable BERT classifier with an accuracy of close to 80\% to classify chat messages during peer tutoring into minimal, facilitative, and constructive, serving as instructional factors. Based on data from 394 students, peer tutor dialog was rare. Only 8\% of tutee problem-solving steps were followed by peer tutor chat messages. Still, facilitative tutor dialog was associated with an increased tutee learning rate. Meanwhile, tutor dialog classified as constructive was associated with lower learning rates. Content analysis suggested that such dialog often reinforced incorrect solutions, gave away answers, or was unrelated to the taught content. Hence, considering problem-solving solution contexts could improve the assessment of peer tutoring dialog. Peer tutors engaging in little dialog could be attributed to the high cognitive demand of learning to tutor while still learning the content they tutor on. Providing peer tutors with instructional support to engage in constructive dialog may improve the tutee's learning.

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Section: Discussionmentioning

confidence: 99%

Combining Dialog Acts and Skill Modeling: What Chat Interactions Enhance Learning Rates During AI-Supported Peer Tutoring?

Borchers,

Yang,

Lin

et al. 2024

Preprint

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“…This makes it possible to test many students at the same time and find patterns and relationships that might be difficult for humans to detect. It has already been seen how these applications help improve teaching, learning, institutional decisions, and guidance, providing valuable information to make more effective and efficient decisions in the educational environment [91].…”

Section: Final Considerationsmentioning

confidence: 99%

Optimizing Education with Data Analytics: A Feature Comparison of LMS and SIS

Rodríguez,

Díaz,

González

2024

Preprint

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In the realm of educational technology, Data Analytics (DA) is reshaping teaching and learning. This paper investigates the impact of DA on education, specifically comparing Student Information Systems (SIS) and Learning Management Systems (LMS). Data-driven insights from SIS and LMS platforms enable educators to tailor instructional strategies to individual student needs effectively. While SIS manages student data for administrative tasks, LMS offers tools for content delivery and collaboration. Using the strengths of SIS and LMS, educators can optimise teaching and learning experiences, tracking student progress, and fostering collaboration. The comparison emphasises their distinct roles: SIS manages administrative tasks, while LMS focusses on content delivery and collaboration. Using both systems, educators improve the teaching and learning experience, leveraging DA for continuous improvement. In summary, this paper underscores the transformative role of DA in education, framed within the context of comparing the SIS and LMS functionalities. By examining the intersection of data-driven decision making and educational technology, educators are equipped with the tools and knowledge to harness the power of data for meaningful and impactful teaching and learning.

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“…Zaki et al proposed an LA conceptual model in serious games for education in Malaysia [28]. Ismail et al identified the LA as a powerful technique for exploring the data generated from the LMS and highlighted the likelihood of handling the LA technique with the LMS engagement in educational institutions [29]. However, LA implementation in Malaysia is fraught with difficulties.…”

Section: Learning Analytics Dashboardsmentioning

confidence: 99%

Predicting the Intention to Use Learning Analytics for Academic Advising in Higher Education

Bahari,

Arpaci,

Azmi

et al. 2023

Sustainability

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Learning analytics (LA) is a rapidly growing educational technology with the potential to enhance teaching methods and boost student learning and achievement. Despite its potential, the adoption of LA remains limited within the education ecosystem, and users who do employ LA often struggle to engage with it effectively. As a result, this study developed and assessed a model for users’ intention to utilize LA dashboards. The model incorporates constructs from the “Unified Theory of Acceptance and Use of Technology”, supplemented with elements of personal innovativeness, information quality, and system quality. The study utilized exploratory research methodology and employed purposive sampling. Participants with prior experience in LA technologies were selected to take part in the study. Data were collected from 209 academic staff and university students in Malaysia (59.33% male) from four top Malaysian universities using various social networking platforms. The research employed “Partial Least Squares Structural Equation Modeling” to explore the interrelationships among the constructs within the model. The results revealed that information quality, social influence, performance expectancy, and system quality all positively impacted the intention to use LA. Additionally, personal innovativeness exhibited both direct and indirect positive impacts on the intention to use LA, mediated by performance expectancy. This study has the potential to offer valuable insights to educational institutions, policymakers, and service providers, assisting in the enhancement of LA adoption and usage. This study’s contributions extend beyond the present research and have the potential to positively impact the field of educational technology, paving the way for improved educational practices and outcomes through the thoughtful integration of LA tools. The incorporation of sustainability principles in the development and deployment of LA tools can significantly heighten their effectiveness, drive user adoption, and ultimately nurture sustainable educational practices and outcomes.

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Exploring Students Engagement Towards the Learning Management System (LMS) Using Learning Analytics

Cited by 26 publications

References 46 publications

Combining Dialog Acts and Skill Modeling: What Chat Interactions Enhance Learning Rates During AI-Supported Peer Tutoring?

Combining Dialog Acts and Skill Modeling: What Chat Interactions Enhance Learning Rates During AI-Supported Peer Tutoring?

Optimizing Education with Data Analytics: A Feature Comparison of LMS and SIS

Predicting the Intention to Use Learning Analytics for Academic Advising in Higher Education

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