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Education Sciences
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Technology-Enhanced Learning in Tertiary Education
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Technology-Enhanced Learning in Tertiary Education

A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Technology Enhanced Education".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 9329

Special Issue Editors

Dr. Soonja Yeom

E-Mail Website
Guest Editor
School of Information and Communication Technology, University of Tasmania, Hobart 7007, Australia
Interests: educational technology; learning analytics; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Dr. Nicole Herbert

E-Mail Website
Guest Editor
School of Information and Communication Technology, University of Tasmania, Hobart 7007, Australia
Interests: ICT curriculum design and development; ICT industry placements within curriculum; student engagement and retention
Dr. Matthew Springer

E-Mail Website
Guest Editor
Information & Communication Technology, University of Tasmania, Hobart, Australia
Interests: ICT in education; information and communication technology; e-learning; technology enhanced learning; computers in education; online learning; online education; e-learning in higher education; blended learning; teaching and learning
Prof. Dr. Soo-Hyung Kim

E-Mail Website
Guest Editor
Pattern Recognition Lab, Chonnam National University, Gwangju, Republic of Korea
Interests: deep-learning-based emotion recognition; medical image analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The landscape of teaching and learning in tertiary education has been revolutionized by rapid technological advancements. Online platforms and digital tools have become integral to delivering education, creating new opportunities for innovation and engagement.

Technology enhances education in numerous ways, including supporting student-centered learning by fostering dynamic interactions among peers, educators, and resources. It also improves accessibility and makes learning more engaging. For educators, technology offers tools to streamline assessment, detect plagiarism, and provide automated feedback. Furthermore, the data generated by learning management systems—when paired with learning analytics—offer deep insights into student behaviors, engagement patterns, and collaborative dynamics.

This Special Issue aims to explore how technology is shaping and enhancing teaching and learning practices in tertiary education.

This collection focuses on the transformative role of technology in improving educational outcomes, fostering inclusivity, and enhancing engagement in tertiary education. We aim to showcase innovative research, practices, and applications that highlight the potential of technology to revolutionize learning experiences.

We invite researchers and educators to contribute original research, case studies, or reviews on the following topics:

  1. Online Learning Design: Exploring innovative strategies and frameworks for effective online education.
  2. Project-Based Learning: Examining how technology facilitates collaborative, hands-on learning experiences.
  3. Inclusive Learning Engagement: Investigating approaches to ensure equitable participation and accessibility in online learning environments.
  4. Technology-Enhanced Learning: Highlighting tools and techniques that transform traditional teaching methods.
  5. AI and Deep Learning in Education: Applying artificial intelligence and emotion-based interactive data to improve learning experiences.
  6. Learning Analytics: Utilizing data to uncover insights into student engagement, performance, and collaboration.

Dr. Soonja Yeom
Dr. Nicole Herbert
Dr. Matthew Springer
Prof. Dr. Soo-Hyung Kim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • educational technology
  • learning analytics
  • retention
  • engagement
  • technology-enhanced learning
  • emotion and learning
  • applied AI techniques in learning
  • emotion detection from students’ comments (interaction with LMS)

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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31 pages, 14734 KB  
Article
Teaching and Learning Trochoid Curves: The Importance of LEGO® Drawing Robots and Educational Robotics in Tertiary Mathematics Education
by Szilvia Szilágyi, Attila Körei and Ingrida Vaičiulytė
Educ. Sci. 2025, 15(11), 1472; https://doi.org/10.3390/educsci15111472 - 3 Nov 2025
Viewed by 684
Abstract
An innovative, STEAM-based educational approach uses LEGO® robots to improve the visualisation and understanding of trochoid curves in tertiary mathematics education. The method involves a two-step process: first, the curves are drawn based on the classical definition of trochoids using a custom-designed [...] Read more.
An innovative, STEAM-based educational approach uses LEGO® robots to improve the visualisation and understanding of trochoid curves in tertiary mathematics education. The method involves a two-step process: first, the curves are drawn based on the classical definition of trochoids using a custom-designed LEGO® robot that employs LED light to trace the shapes. Then, the same process is replicated with a marker, with the robot controlling the movement of the drawing head to reproduce the curves accurately. To deepen students’ comprehension and visualisation, Desmos dynamic geometry software was used in parallel to draw all three types of trochoids (prolate, curtate, and cusped). This hands-on technique aims to make these motion curves more tangible and engaging within a classroom setting. A quantitative experiment involving 94 first-year IT BSc students was conducted during the spring semester of the 2024/2025 academic year using a quasi-experimental design. We had one control group and two experimental groups. One of the experimental groups did not use educational robotics; participants could only see how the robots worked via video. The other experimental group gained first-hand experience by building and testing LEGO® drawing robots. The aim was to evaluate the effectiveness of an innovative teaching method that used educational robotics to improve understanding of the mathematical properties of trochoids, compared to traditional teaching methods and presentations containing short videos. The Mann–Whitney U test was used in all cases during hypothesis testing. Only watching videos of drawing robots does not have a statistically significant effect on learning outcomes. In this case, the effect size was only 0.12. However, the results of the group performing educational robotics activities showed a statistically significant difference compared to the other two groups, with large effect sizes (0.68 and 0.7). Our results suggest that visualisation using LEGO® robots significantly improves students’ knowledge of parametric curves. Educational robotics offers promising opportunities because it is an attractive and interactive teaching tool. Its great advantage is that it combines abstract mathematical concepts with modern technology, thus creating an effective learning environment. Full article
(This article belongs to the Special Issue Technology-Enhanced Learning in Tertiary Education)
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25 pages, 478 KB  
Article
Closing the Gap? The Ability of Adaptive Learning Courseware to Close Outcome Gaps in Principles of Microeconomics
by Karen Gebhardt and Christopher D. Blake
Educ. Sci. 2024, 14(9), 1025; https://doi.org/10.3390/educsci14091025 - 19 Sep 2024
Cited by 2 | Viewed by 2176
Abstract
Research shows that students who identify as low-income, first-generation, and/or racially diverse disproportionately underperform in college and earn fewer degrees than other students. This study explores the integration of adaptive learning courseware assignments as a tool to help close these outcome gaps and [...] Read more.
Research shows that students who identify as low-income, first-generation, and/or racially diverse disproportionately underperform in college and earn fewer degrees than other students. This study explores the integration of adaptive learning courseware assignments as a tool to help close these outcome gaps and to ensure more equitable learning across diverse student groups. Adaptive learning courseware is an educational technology that requires students to master the same learning objectives but, for each student, the courseware determines the order and timing of content based on how that student interacts with the courseware, thus enabling an individualized learning path for each student. Adaptive learning assignments were implemented in five sections of a highly-enrolled Principles of Microeconomics course at a medium-sized state university in the United States. This study draws from student data (n=581), which includes adaptive learning assignment completion data, detailed exam and final grade data, and institutional demographic data. Descriptive statistics and regression analyses are used to explore if the completion of adaptive learning assignments disproportionately benefited low-income, first-generation, or racially diverse students, thus helping close the gap between students from different backgrounds. Findings include significant evidence that adaptive learning assignment completion was correlated with more exam questions answered correctly by all students, with this correlation being disproportionately stronger for students who identify as being from a minority background and for foundational (easy) exam questions. Full article
(This article belongs to the Special Issue Technology-Enhanced Learning in Tertiary Education)
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26 pages, 1667 KB  
Article
Enhancing ICT Literacy and Achievement: A TPACK-Based Blended Learning Model for Thai Business Administration Students
by Cherisa Nantha, Kobchai Siripongdee, Surapong Siripongdee, Paitoon Pimdee, Thiyaporn Kantathanawat and Kanitphan Boonsomchuae
Educ. Sci. 2024, 14(5), 455; https://doi.org/10.3390/educsci14050455 - 25 Apr 2024
Cited by 7 | Viewed by 4768
Abstract
The COVID-19 pandemic has heightened the need for 21st century skills, particularly computer and ICT literacy (CICT) in Thailand. This study aimed to develop a TPACK (Technological Pedagogical and Content Knowledge)-based blended learning model (BLM) to enhance CICT skills and academic performance among [...] Read more.
The COVID-19 pandemic has heightened the need for 21st century skills, particularly computer and ICT literacy (CICT) in Thailand. This study aimed to develop a TPACK (Technological Pedagogical and Content Knowledge)-based blended learning model (BLM) to enhance CICT skills and academic performance among 179 Business Administration (BA) undergraduates in the 2022 academic year Computer and Information Applications course. Research instruments were designed and evaluated by experts. Over 18 weeks, qualitative and quantitative data were collected, with the qualitative data undergoing content analysis. Descriptive statistics were used to analyze quantitative data, comparing pretests, post-tests, and 2-week retests using a repeated measure ANOVA. One-sample t-tests were used to assess the model’s impact on CICT skills. The results showed a significant score improvement between tests, with the highest mean being seen in the 2-week retest. The BA-TPACK model significantly enhanced CICT skills, exceeding 80%. The students expressed high satisfaction, with the BA-TPACK model effectively enhancing CICT skills and academic achievement, recommending its integration into future computer and information courses. This study’s contribution lies in addressing the pressing need for CICT skills in the ‘new normal’. By developing and implementing a BLM grounded in the TPACK framework, this study not only enhances students’ CICT proficiency but also fills a crucial gap in the literature regarding effective pedagogical approaches to foster 21st century skills. Full article
(This article belongs to the Special Issue Technology-Enhanced Learning in Tertiary Education)
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Other

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38 pages, 1280 KB  
Systematic Review
Improve Student Risk Prediction with Clustering Techniques: A Systematic Review in Education Data Mining
by Yuan Lu, Soonja Yeom, Jamal Maktoubian, Mohammad Mustaneer Rahman and Soo-Hyung Kim
Educ. Sci. 2025, 15(12), 1695; https://doi.org/10.3390/educsci15121695 - 15 Dec 2025
Abstract
Student dropout rates continue to present major difficulties for educational institutions, leading to academic, operational, and financial impacts. Educational Data Mining (EDM) methods, particularly those combining clustering techniques with predictive models, have demonstrated potential in identifying at-risk students early and accurately. This systematic [...] Read more.
Student dropout rates continue to present major difficulties for educational institutions, leading to academic, operational, and financial impacts. Educational Data Mining (EDM) methods, particularly those combining clustering techniques with predictive models, have demonstrated potential in identifying at-risk students early and accurately. This systematic review explores how cluster-based prediction models have been applied in educational contexts to enhance student performance prediction. A total of sixty-one relevant studies published between 2010 and 2025 were selected and analysed using PRISMA guidelines. The review focuses on the clustering techniques used, how these are integrated with predictive models, and what types of student data are involved. Key findings show that cluster-based models help capture behavioural and academic differences among students, which enables educational institutions to provide more adaptable support. The review also highlights challenges related to generalisability, scalability, and ethical concerns, especially when applying models across different institutions or datasets. The main contribution of this study is the identification of how clustering can be used not only to segment student populations but also to improve prediction accuracy by tailoring models to each subgroup. This review contributes to the literature by emphasising the practical benefits of cluster-based predictive modelling and providing clear directions for further studies aimed at reducing student dropout through targeted support. Full article
(This article belongs to the Special Issue Technology-Enhanced Learning in Tertiary Education)
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