Mobile Learning Analytics for Data Science-Driven Cognitive Skill Development in Computer Science Engineering

Srividhya S.; Ranjani M.; Rajesh Kumar K.; Praveenkumar R.; Ramakrishnan P.

doi:10.3991/ijim.v20i10.61589

Authors

Srividhya S. SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
Ranjani M. SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India https://orcid.org/0000-0002-9162-4956
Rajesh Kumar K. SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India https://orcid.org/0000-0003-0411-020X
Praveenkumar R. Nandha Engineering College, Erode, Tamil Nadu, India
Ramakrishnan P. M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India

DOI:

https://doi.org/10.3991/ijim.v20i10.61589

Keywords:

mobile learning analytics, cognitive skill development, data science in education, learning analytics, educational data mining, XGBoost, self-regulated learning, at-risk prediction, engineering education, quasi-experimental design

Abstract

The current CSE curriculum must cater to the rising interest in data science competencies, which requires teaching and learning models that structurally strengthen higher-order cognitive skills. In this paper, we propose a data science–orientated mobile learning analytics (MLA) framework that aims to support undergraduate CSE students in developing critical thinking, problem-solving, analytical reasoning, self-regulated learning, and knowledge retention skills through empirical validation. Using a purpose-built mobile learning platform, the 16-week quasiexperimental study engaged 120 undergraduate students (experimental group: n = 62; control group: n = 58) and extracted multimodal learner data including interaction logs, formative assessment records, behavioural engagement metrics and self-regulatory survey information. Techniques from learning analytics, including statistical inference and machine learning–based predictive modelling, were used to analyse learner trajectories and identify at-risk students. Independent-samples t-tests showed statistically significant improvements in the experimental group on all five dimensions of cognition (p < .001), with Cohen’s d effect sizes between 0.97 and 1.19 reflecting large practical significance. A gradient boosting classifier based on XGBoost attained a learning accuracy of 89.2% (AUC–ROC = 0.931) in identifying at-risk learners and allowed for timely personalised interventions, resulting in an early intervention success rate of 72.7% among flagged learners who managed to cross above the risk threshold by midsemester. This paper proposes an MLA framework to create a scalable pedagogy that provides coherence between Bloom’s revised taxonomy, Zimmermann’s model of self-regulated learning (SRL) and the SAMR model. The findings substantively advance the empirical knowledge base for data science–enabled engineering education and provide evidence-based guidance to inform curriculum designers and educators who implement learner-centred, analytically augmented pedagogical strategies in data-intensive computing programmes.

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