Predicting Student Performance from Their Behavior in Learning Management Systems

Abstract

Nowadays, Information and Communication Technology (ICT) provides an opportunity to discover new knowledge and create a desirable learning environment. That is why the influence of ICT on education is irrefutable. Technology has changed the learning styles: the way people prefer to learn and improve the quality of their learning. Physical and online classes can be held concurrently so that lecturers and students can interact via learning management systems. A Learning Management System (LMS) is an application software that plays a significant role in educational technology. Such software can be designed to augment and facilitate instructional activities including registration and management of education courses, analyzing skill gaps, reporting, and delivery of electronic courses concurrently. Since all information and corresponding data are recorded and monitored in the LMS, it can provide an accurate insight into student's online behavior. In general, measuring student performance is an important part of the education system. The fields of learning analytics and educational data mining both emphasize the analysis of educational data in order to improve teaching and learning styles as well as to predict student performance. In the current study, we use data from the Moodle LMS from a collection of courses from a single institution to identify weak/strong students during the course. The result has to be interpretable and understandable as the aim is to give this information to lecturers, who may use the information to improve their course and identify students who may need special attention.

An application of Bayesian inference to examine student retention and attrition in the STEM classroom

“…The inclusion of LMS data in EDM and LA models have been predominantly utilized in online, blended, or flipped classroom environments where they were deemed necessary tools for guiding administrative and pedagogical interventions (see Al-Shabandar et al, 2017;Wang, 2017;Lisitsyna and Oreshin, 2019;Shayan and van Zaanen, 2019;Louhab et al, 2020;Nieuwoudt, 2020). Our findings demonstrated that using technological resources with in-class instruction provided greater insights into student achievement.…”

Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022)

“…Most of the recent studies employed students’ online behavioral data in the prediction since most of the experiments were conducted in an online learning environment or a blended setting. Some of the examples are presented in (Abdullah et al, 2021 ; Cerezo et al, 2017 ; Lu et al, 2018 ; Mansouri et al, 2021 ; Shayan & van Zaanen, 2019 ), authors used students’ behaviors logs containing their interactions with the online content to assess their performance, while Ayouni et al ( 2021 ) and Hussain et al ( 2018 ) used it to measure students’ levels of engagement. (Chen & Cui, 2020 ; Chen et al, 2020 ; Dass et al, 2021 ; Macarini et al, 2019 ) utilized features related to time spent on online materials to identify at-risk of failing students or the ones who are most likely to dropout.…”

A interdisciplinaridade no desempenho da nota de matemática: um olhar para evolução do processo de ensino por meio de modelos regressivos

“…Como Chiheb et al [2017] que analisam alunos de graduação e de pós-graduação, classificando-os de acordo com os resultados, que podem ser utilizados pela gestão para indicar os discentes em risco de evasão. Já Shayan and van Zaanen [2019] verificam o comportamento do aluno no Sistema de Gerenciamento de Aprendizado a fim de identificar diferentes desempenhos durante o curso e, assim, apresentar os resultados aos docentes que poderão usar as informações para aprimorar os conteúdos das disciplinas e distinguir aqueles que necessitem de maior atenção.…”