Mobile Learning Analytics for Data Science-Driven Cognitive Skill Development in Computer Science Engineering.
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| Title: | Mobile Learning Analytics for Data Science-Driven Cognitive Skill Development in Computer Science Engineering. |
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| Authors: | S., Srividhya1 srividhs1@srmist.edu.in, M., Ranjani1 ranjanim1@srmist.edu.in, K., Rajesh Kumar2 errajeshmba@gmail.com, R., Praveenkumar3 rpraveenster@gmail.com, P., Ramakrishnan4 ramakrishnanp.ece@mkce.ac.in |
| Source: | International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 10, p16-29. 14p. |
| Subjects: | Learning analytics, Data science, Mobile learning, Cognitive development, Self-regulated learning, Educational technology, Prediction models, Computer science |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194196187 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mobile Learning Analytics for Data Science-Driven Cognitive Skill Development in Computer Science Engineering. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22S%2E%2C+Srividhya%22">S., Srividhya</searchLink><relatesTo>1</relatesTo><i> srividhs1@srmist.edu.in</i><br /><searchLink fieldCode="AR" term="%22M%2E%2C+Ranjani%22">M., Ranjani</searchLink><relatesTo>1</relatesTo><i> ranjanim1@srmist.edu.in</i><br /><searchLink fieldCode="AR" term="%22K%2E%2C+Rajesh+Kumar%22">K., Rajesh Kumar</searchLink><relatesTo>2</relatesTo><i> errajeshmba@gmail.com</i><br /><searchLink fieldCode="AR" term="%22R%2E%2C+Praveenkumar%22">R., Praveenkumar</searchLink><relatesTo>3</relatesTo><i> rpraveenster@gmail.com</i><br /><searchLink fieldCode="AR" term="%22P%2E%2C+Ramakrishnan%22">P., Ramakrishnan</searchLink><relatesTo>4</relatesTo><i> ramakrishnanp.ece@mkce.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 10, p16-29. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+science%22">Data science</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+learning%22">Mobile learning</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+development%22">Cognitive development</searchLink><br /><searchLink fieldCode="DE" term="%22Self-regulated+learning%22">Self-regulated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijim.v20i10.61589 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 16 Subjects: – SubjectFull: Learning analytics Type: general – SubjectFull: Data science Type: general – SubjectFull: Mobile learning Type: general – SubjectFull: Cognitive development Type: general – SubjectFull: Self-regulated learning Type: general – SubjectFull: Educational technology Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Computer science Type: general Titles: – TitleFull: Mobile Learning Analytics for Data Science-Driven Cognitive Skill Development in Computer Science Engineering. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: S., Srividhya – PersonEntity: Name: NameFull: M., Ranjani – PersonEntity: Name: NameFull: K., Rajesh Kumar – PersonEntity: Name: NameFull: R., Praveenkumar – PersonEntity: Name: NameFull: P., Ramakrishnan IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18657923 Numbering: – Type: volume Value: 20 – Type: issue Value: 10 Titles: – TitleFull: International Journal of Interactive Mobile Technologies Type: main |
| ResultId | 1 |
