Interpretable and Adaptable Early Warning Learning Analytics Model

Major issues currently restricting the use of learning analytics are the lack of interpretability and adaptability of the machine learning models used in this domain.Interpretability makes it easy for the stakeholders to understand the working of these models and adaptability makes it easy to use the same model for multiple cohorts and courses in educational institutions.Recently,some models in learning analytics are constructed with the consideration of interpretability but their interpretability is not quantified.However,adaptability is not specifically considered in this domain.This paper presents a new framework based on hybrid statistical fuzzy theory to overcome these limitations.It also provides explainability in the form of rules describing the reasoning behind a particular output.The paper also discusses the system evaluation on a benchmark dataset showing promising results.The measure of explainability,fuzzy index,shows that the model is highly interpretable.This system achieves more than 82%recall in both the classification and the context adaptation stages.

Developing Engagement in the Learning Management System Supported by Learning Analytics

Learning analytics is an emerging technique of analysing student par-ticipation and engagement.The recent COVID-19 pandemic has significantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to 2020.To date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and instructors.Consequently,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notfirst identified.Therefore,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an LMS.The required data was collected from an LMS that is currently utilised by a local Malaysian university.The model was validated by a panel of experts as well as discussions with students.It is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

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

Learning analytics is a rapidly evolving research discipline that uses theinsights generated from data analysis to support learners as well as optimize boththe learning process and environment. This paper studied students’ engagementlevel of the Learning Management System (LMS) via a learning analytics tool,student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review(SLR) was employed for the selection, sorting and exclusion of articles fromdiverse renowned sources. The findings show that most of the engagement inLMS are driven by educators. Additionally, we have discussed the factors inLMS, causes of low engagement and ways of increasing engagement factorsvia the Learning Analytics approach. Nevertheless, apart from recognizing theLearning Analytics approach as being a successful method and technique for analyzing the LMS data, this research further highlighted the possibility of mergingthe learning analytics technique with the LMS engagement in every institution asbeing a direction for future research.

Requirement-driven Architecture for Learning Analytics:The Case of AT41 Project

Digital technologies are becoming present and essential in all sectors of our lives.In education,the intensive usage of digital learning devices contributes to generating a large amount of trace data from digital learning activities.Intelligent exploitation of these traces represents a valuable asset for both device producers(to improve the design of the devices)and consumers(learners and teachers).In this paper,we first share our vision for better exploitation by teachers,of traces from middle schoolers’digital activities generated by their use of tools and digital learning services during different classes.This vision is a part of the AT41 project funded by the French Ministry of Education.This exploitation has to meet the requirements of the different teachers.Conducting such a project is not an easy task,because it has to consider the following issues:①the lack of comprehensive and clear methodology to design and exploit these traces;①heterogeneity of teacher requirements that complicates their elicitation and analysis;①the diversity of trace sources.Secondly,we propose a requirement-driven architecture for Learning Analytics composed of a well-identified life cycle.This architecture is augmented by learner traces.It offers a repository storing both teacher requirements and traces to facilitate the Learning Analytics in generating relevant and valuable indicators.

Students’ Numeracy and Literacy Aptitude Analysis and Prediction Using Machine Learning

Education is one of the most pivotal services in societal development as it cultivates a wide variety of skills, especially numeracy and literacy skills. However, students may have varying masteries of these two aptitudes. Some attribute this to students’ intrinsic efforts while others attribute this to students’ capabilities and affiliated environments. In this work, I explore the numeracy and literacy aptitude patterns of students from various cultures based on a dataset that contains various demographic information, from which I deduced some preliminary trends. After the comparison of numerous machine learning algorithms, the optimal algorithm or combination of a few algorithms predicts students’ performances by classifying students of different backgrounds into various potential outcomes. The results suggest that proper resources and supports are necessary for enhanced learning.

PerformanceVis:Visual analytics of student performance data from an introductory chemistry course

We present PerformanceVis,a visual analytics tool for analyzing student admission and course performance data and investigating homework and exam question design.Targeting a university-wide introductory chemistry course with nearly 1000 student enrollment,we consider the requirements and needs of students,instructors,and administrators in the design of PerformanceVis.We study the correlation between question items from assignments and exams,employ machine learning techniques for student grade prediction,and develop an interface for interactive exploration of student course performance data.PerformanceVis includes four main views(overall exam grade pathway,detailed exam grade pathway,detailed exam item analysis,and overall exam&homework analysis)which are dynamically linked together for user interaction and exploration.We demonstrate the effectiveness of PerformanceVis through case studies along with an ad-hoc expert evaluation.Finally,we conclude this work by pointing out future work in this direction of learning analytics research.

Chilean University Students Digital Learning Technology Usage Patterns and Approachesto Learning

Purpose:This study aims to explore Chilean students'digital technology usage patterns andapproaches to learningDesignlApproach/Methods:We conducted this study in two stages We worked with onesemester learning management systems(LMS),library,and students records data in the firstone.We performed a k-means cluster analysis to identify groups with similar usage patterns.Inthe second stage,we invited students from emerging dusters to participate in group interviews.Thematic analysis was employed to analyze them.Findings:Three groups were identified:ID digital library users/high performers,who adopteddeeper approaches to learning obtained higher marks,and used learning resources to integratematerials and expand understanding 2)LMS and physical library userslmid-performers,whoadopted mainly strategicapproaches obtained marks dlose to average,and used learning resources for studying in an organized manner toget good marks and 3)lower users of LMS andlibrarylmidlow performers,who adopted mainly a surface approach,obtained mid-to-lower-than-averagemarks,and used learning resources for minimum content understanding Originality/Value:We demonstrated the importance of combining learning analytics data withqualitative methods to make sense of digital technology usage patternss approaches to learningare associated with learning resources use.Practical recommendations are presented.

A Study of Learning Behavioral Engagement in Live Teaching

Based on the related theories and research results of learning behavioral engagement,this study constructed an evaluation framework of learning behavioral engagement in live teaching,which included 24 indicators in three dimensions:compliance with norms,learning participation and social participation.A small-class live English learning for younger students on the ClassIn was taken as a case study program.Five younger students attended this English learning course of 16 lessons totaling 950 minutes.The preset indicators were preliminarily examined based on the teaching records and the recorded course data.Then,experts in the field of educational technology were invited to develop the learning behavioral engagement dimensions and indicator weightings by using the Analytic Hierarchy Process,and to determine the evaluation indicator system for the evaluation of learning behavioral engagement.Finally,based on this framework,the characteristics of learning behavioral engagement of the case course were analyzed,and the influences of students’individual factors,teaching and environmental factors on learning behavioral engagement in live teaching were investigated.

A Study of Detecting Student Engagement Modes in an Online Learning Platform

In this paper,we used the platform log data to extract three features(proportion of passive video time,proportion of active video time,and proportion of assignment time)aligning with different learning activities in the Interactive-Constructive-Active-Passive(ICAP)framework,and applied hierarchical clustering to detect student engagement modes.A total of 840 learning rounds were clustered into four categories of engagement:passive(n=80),active(n=366),constructive(n=75)and resting(n=319).The results showed that there were differences in the performance of the four engagement modes,and three types of learning status were identified based on the sequences of student engagement modes:difficult,balanced and easy.This study indicated that based on the ICAP framework,the online learning platform log data could be used to automatically detect different engagement modes of students,which could provide useful references for online learning analysis and personalized learning.