Aligning Learners' Expectations and Performance by Learning Analytics Systemwith a Predictive Model

TL;DR

This paper presents a learning analytics system with a predictive model that aligns student expectations and performance, supporting self-regulated learning and early intervention in higher education.

Contribution

It introduces a requirement-oriented LA solution with a grade prediction AI model tailored to students' expectations, filling a gap in Slovenian higher education.

Findings

Grade prediction precision reaches 98% after one month.

Mean absolute error in grade prediction is 11.2 points.

Initial student feedback was positive.

Abstract

Learning analytics (LA) is data collection, analysis, and representation of data about learners in order to improve their learning and performance. Furthermore, LA opens the door to opportunities for self-regulated learning in higher education, a circular process in which learners activate and sustain behaviours that are oriented toward their personal learning goals. The potentials of LA and self-regulated learning are huge; however, they are not yet widely applied in higher education institutions. Slovenian higher education institutions have lagged behind other European countries in LA adoption. Our research aims to fill this gap by using a qualitatively and quantitatively led workflow for building a requirement-oriented LA solution, consisting of empirically gathering the students' expectations of LA and presenting a dashboard solution. Translated Student Expectations of Learning Analytics Questionnaire and focus groups were used to gather expectations from learners. Based on them, a user interface utilizing LA and grade prediction with an AI model was implemented for a selected course. The interface includes early grade prediction, peer comparison, and historical data overview. Grade prediction is based on a machine learning model built on users' interaction in the virtual learning environment, demographic data and lab grades. First, classification is used to determine students at risk of failing - its precision reaches 98% after the first month of the course. Second, the exact grade is predicted with the Decision Tree Regressor, reaching a mean absolute error of 11.2grade points (on a 100 points scale) after the first month. The proposed system's main benefit is the support for self-regulation of the learning process during the semester, possibly motivating students to adjust their learning strategies to prevent failing the course. Initial student evaluation showed positive results.