Stability and sensitivity of Learning Analytics based prediction models

Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In two cohorts of a large introductory quantitative methods module, 2049 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation

Unravelling the dynamics of learning design within and between disciplines in higher education using learning analytics

Designing effective learning experience in virtual learning environment (VLE) can be supported by learning analytics (LA) through explicit feedback on how learning design (LD) influences students’ engagement, satisfaction and performance. Marrying LA with LD not only puts existing pedagogical theories in instructional design to the test with actual learning data, but also provides the context of learning which helps educators translate established LA findings to direct interventions. My dissertation aims at unpacking the complexity of LD and its impact on students’ engagement, satisfaction and performance on VLE using LA. The context of this study is 400+ online and blended learning modules at the Open University (OU) UK. This research combines multiple sources of data from the OU Learning Design Initiative (OULDI), system log data, self-reported surveys, and performance data. Given the scope of this study, a wide range of visualization techniques, social network analysis, multi-level modelling, and machine learning will be used

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Categorization of Learning Analytics Models: Brief Literature Review

Learning analytics is one of the technological tools aiming to investigate educational database collected during the learning delivery process for further purpose of use in decision making or process update. Various types of methods on learning analytics are originated by scholars with their own ambition to contribute the field study. It is emerging study field since 2010s. This paper review literature papers which focused on categorization of learning analytics models with focus of its’ criteria. The papers are chosen from open scholar databases. The selected papers reviewed learning analytics model related studies to bring up their suggested categorization. The category based on the learning analytics models’ main objective as well as used approach. It is observed that prediction of student achievement or success is significant method among learning analytics models

Exploring Learning Analytics In E-Learning: A Comprehensive Analysis of Student Characteristics and Behavior

This article aims to explore learning analytics in e-learning through a comprehensive analysis of student characteristics and behavior. E-learning has become increasingly significant in education, particularly due to the social situation influenced by the pandemic. The Learning Management System (LMS) has become a crucial tool for educators to track and record student learning in e-learning environments. Learning analytics can aid in understanding the context of students, ensuring that they receive a personalized learning experience aligned with learning objectives. However, educators often face challenges in conducting learning analytics for e-learning students, primarily due to the large number of students to analyze and limited data availability. This study seeks to provide a detailed description of e-learning students within the Open and Distance Education (ODE) system. ODE students exhibit high diversity in demographic profiles, learning behaviors, and competency backgrounds. To support this research, we utilize datasets containing student demographic profiles and learning activity data during e-learning sessions. The datasets are obtained from the academic system and LMS log data of Universitas Terbuka. The article employs Exploratory Data Analysis (EDA) and data science approaches as the foundation for predictive and prescriptive analytics of student learning outcomes. Relevant features are extracted from the dataset to build a robust predictive model. The analysis results present patterns and relationships between student characteristics, learning behaviors, and academic achievements. This research aims to provide valuable insights for the development of more effective and personalized e-learning strategies to enhance student learning outcomes in the context of distance education. Moreover, the analysis findings can serve as a basis for informed academic decision-making to improve the quality of e-learning environments

IDENTIFICATION OF STUDENTS AT RISK OF LOW PERFORMANCE BY COMBINING RULE-BASED MODELS, ENHANCED MACHINE LEARNING, AND KNOWLEDGE GRAPH TECHNIQUES

Technologies and online learning platforms have changed the contemporary educational paradigm, giving institutions more alternatives in a complex and competitive environment. Online learning platforms, learning-based analytics, and data mining tools are increasingly complementing and replacing traditional education techniques. However, academic underachievement, graduation delays, and student dropouts remain common problems in educational institutions. One potential method of preventing these issues is by predicting student performance through the use of institution data and advanced technologies. However, to date, scholars have yet to develop a module that can accurately predict students’ academic achievement and commitment. This dissertation attempts to bridge that gap by presenting a framework that allows instructors to achieve four goals: (1) track and monitor the performance of each student on their course, (2) identify at-risk students during the earliest stages of the course progression (3), enhance the accuracy with which at-risk student performance is predicted, and (4) improve the accuracy of student ranking and development of personalized learning interventions. These goals are achieved via four objectives. Objective One proposes a rule-based strategy and risk factor flag to warn instructors about at-risk students. Objective Two classifies at-risk students using an explainable ML-based model and rule-based approach. It also offers remedial strategies for at-risk students at each checkpoint to address their weaknesses. Objective Three uses ML-based models, GCNs, and knowledge graphs to enhance the prediction results. Objective Four predicts students’ ranking using ML-based models and clustering-based KGEs with the aim of developing personalized learning interventions. It is anticipated that the solution presented in this dissertation will help educational institutions identify and analyze at-risk students on a course-by-course basis and, thereby, minimize course failure rates

A Proposed Artificial Intelligence-Based System for Developing E-management Skills in Saudi Primary Schools

This study aims to investigate the impact of Artificial intelligence-driven solutions on school leaders’ proficiencies. Leaders have the responsibility of making decisions in educational institutions as well as carrying out routine tasks daily. Artificial intelligence-assisted applications have noteworthy contributions to the field of educational management. The scope of this study is limited to selected features; data analytics, chatbot, and e-survey. The basic design of this study started with analyzing literature in this domain. This was followed by designing a system consisting of four models: building a dashboard, predicting students’ results, creating a chatbot for responding to parents’ queries and creating an e-survey for measuring staff satisfaction. The participants of this study consist of 35 school leaders, whereas the sample was one group that was exposed to special treatment. A pre/post-test was conducted to examine the impact of the treatment, in addition to an observation card that was used to measure the treatment’s impact on the technical domains. The researchers used SPSS to analyze the study’s results. The prominent finding of this study is the significant impact of Artificial intelligence on leaders’ competencies; the difference between mean scores in both pre and post-test application and post-application for the observation card has proven the positive impact of the proposed treatment