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Innovating Learning Analytics for Sustainable Higher Education

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Education and Approaches".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 16761

Special Issue Editors


E-Mail Website
Guest Editor
Learning and Teaching Unit, Queensland University of Technology, Brisbane, Australia
Interests: learning analytics; problem-based learning; blended learning; learning design; learning theory

E-Mail Website
Guest Editor
Learning and Teaching Unit, Queensland University of Technology, Brisbane, Australia
Interests: learning analytics; learning design; technology-enhanced learning; collaborative learning

Special Issue Information

Dear Colleagues,

Learning analytics has offered much promise for learning and teaching in recent years, and it has gained a key place in debates around learning and teaching improvement. Indeed, it has become a field of study on its own, with its own conferences and journal. However, similar to the introduction of other technologies in education, the delivery on its promise has been slow, as educational institutions have not adopted learning analytics in a comprehensive manner, in a way that followed the initial expectations. This is partly because of the potential complexity of its institutional adoption. Implementing learning analytics in a meaningful and sustainable manner across an institution requires input from a wide range of stakeholders with a diverse range of expertise, which has often proven to be a challenge. However, the potential that learning analytics offers for the sustainable improvement of learning and teaching in educational institutions is important, and therefore deserves exploration of how best to implement and deploy learning analytics in a sustainable manner. 

The focus of this Special Issue is the sustainable deployment and implementation of learning analytics so as to improve and add value to learning and teaching in higher education, with a particular focus on learning design and pedagogy. 

The scope is limited to learning and teaching in higher education, in order to keep a clear focus for the Special Issue. The purpose of the Special Issue is to identify the practical ways in which learning analytics can be implemented in order to add value to learning and teaching, and thus to improve practice in a sustainable manner

References:

AEHE, Lester, J., Klein, C., Rangwala, H., & Johri, A. (2018). Learning Analytics in Higher Education, John Wiley & Sons, Incorporated, Newark.

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x

Ifenthaler, D., Mah, D., & Yau, J. (2019). Utilizing Learning Analytics to Support Study Success. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-64792-0

Knobbout, J., & van der Stappen, E. (2018). Where Is the Learning in Learning Analytics? A Systematic Literature Review to Identify Measures of Affected Learning (Vol. 11082, pp. 88–100). Springer Verlag. https://doi.org/10.1007/978-3-319-98572-5_7

Lodge, J., Horvath, J., & Corrin, L. (2019). Learning analytics in the classroom: translating learning analytics research for teachers. Abingdon, Oxon: Routledge.

Mangaroska, K. & Giannakos, M. N. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies. https://doi.org/10.1109/TLT.2018.2868673

McKenney, S., & Mor, Y. (2015). Supporting teachers in data‐informed educational design. British Journal of Educational Technology46(2), 265–279. https://doi.org/10.1111/bjet.12262

Pardo, A. (2019). Analytics for learning design: A layered framework and tools. British Journal of Educational Technology, 50(1), 139–152. https://doi.org/10.1111/bjet.12645

Peña-Ayala, A. (2017). Learning Analytics: Fundaments, Applications, and Trends A View of the Current State of the Art to Enhance e-Learning. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-52977-6

Thille, C. and Zimmaro, D. (2017). Incorporating Learning Analytics in the Classroom. New Directions for Higher Education, 2017: 19-31. https://doi:10.1002/he.20240

Viberg, O., Hatakka, M., BäLter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027

Dr. Henk Huijser
Mr. Roger Cook
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • learning analytics
  • learning design
  • evidence-based learning and teaching
  • higher education
  • learning technology

Published Papers (5 papers)

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Research

20 pages, 1860 KiB  
Article
Cognitive Learning Analytics Using Assessment Data and Concept Map: A Framework-Based Approach for Sustainability of Programming Courses
by Uzma Omer, Muhammad Shoaib Farooq and Adnan Abid
Sustainability 2020, 12(17), 6990; https://doi.org/10.3390/su12176990 - 27 Aug 2020
Cited by 24 | Viewed by 3606
Abstract
Students of initial level programming courses generally face difficulties while learning the programming concepts. The learning analytics studies, in these courses, are mostly anecdotal on the aspect of assessment as less or no attention is given to assess learning at various cognitive levels [...] Read more.
Students of initial level programming courses generally face difficulties while learning the programming concepts. The learning analytics studies, in these courses, are mostly anecdotal on the aspect of assessment as less or no attention is given to assess learning at various cognitive levels of specific concepts. Furthermore, the existing work reflects deficiencies in examining the effect of learners’ cognitive performance on subsequent stages of the course. This gap needs to be addressed by introducing more granular and methodical approaches of cognitive analysis for sustaining the programming courses effectively in computer science and associated disciplines. In this article, a framework-based approach is proposed for cognitive learning analytics on the concepts taught in initial level programming courses. The framework serves as a platform that provides structure to the concept data using the technique of concept mapping and examines learners’ cognitive propagation on related concepts using assessment data. Learners’ performance prediction has been examined on relatively higher-level programming concepts through the metrics established from the cognitive maps of learners, acquired by deploying the related layers of framework. Overall maximum prediction accuracy range obtained was 64.81% to 90.86%, which was better than the prediction accuracies presented in most of the related studies. Full article
(This article belongs to the Special Issue Innovating Learning Analytics for Sustainable Higher Education)
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23 pages, 3102 KiB  
Article
Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers
by Syed Muhammad Raza Abidi, Wu Zhang, Saqib Ali Haidery, Sanam Shahla Rizvi, Rabia Riaz, Hu Ding and Se Jin Kwon
Sustainability 2020, 12(15), 6074; https://doi.org/10.3390/su12156074 - 28 Jul 2020
Cited by 12 | Viewed by 3272
Abstract
Ubiquitous online learning is continuing to expand, and the factors affecting success and educational sustainability need to be quantified. Procrastination is one of the compelling characteristics that students observe as a failure to achieve the weaker outcomes. Past studies have mainly assessed the [...] Read more.
Ubiquitous online learning is continuing to expand, and the factors affecting success and educational sustainability need to be quantified. Procrastination is one of the compelling characteristics that students observe as a failure to achieve the weaker outcomes. Past studies have mainly assessed the behaviors of procrastination by describing explanatory work. Throughout this research, we concentrate on predictive measures to identify and forecast procrastinator students by using ensemble machine learning models (i.e., Logistic Regression, Decision Tree, Gradient Boosting, and Forest). Our results indicate that the Gradient Boosting autotuned is a predictive champion model of high precision compared to the other default and hyper-parameterized tuned models in the pipeline. The accuracy we enumerated for the VALIDATION partition dataset is 91.77 percent, based on the Kolmogorov–Smirnov statistics. Additionally, our model allows teachers to monitor each procrastinator student who interacts with the web-based e-learning platform and take corrective action on the next day of the class. The earlier prediction of such procrastination behaviors would assist teachers in classifying students before completing the task, homework, or mastery of a skill, which is useful and a path to developing a sustainable atmosphere for education or education for sustainable development. Full article
(This article belongs to the Special Issue Innovating Learning Analytics for Sustainable Higher Education)
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17 pages, 3876 KiB  
Article
Early Prediction of a Team Performance in the Initial Assessment Phases of a Software Project for Sustainable Software Engineering Education
by Mehwish Naseer, Wu Zhang and Wenhao Zhu
Sustainability 2020, 12(11), 4663; https://doi.org/10.3390/su12114663 - 08 Jun 2020
Cited by 13 | Viewed by 2889
Abstract
Software engineering is a competitive field in education and practice. Software projects are key elements of software engineering courses. Software projects feature a fusion of process and product. The process reflects the methodology of performing the overall software engineering practice. The software product [...] Read more.
Software engineering is a competitive field in education and practice. Software projects are key elements of software engineering courses. Software projects feature a fusion of process and product. The process reflects the methodology of performing the overall software engineering practice. The software product is the final product produced by applying the process. Like any other academic domain, an early evaluation of the software product being developed is vital to identify the at-risk teams for sustainable education in software engineering. Guidance and instructor attention can help overcome the confusion and difficulties of low performing teams. This study proposed a hybrid approach of information gain feature selection with a J48 decision tree to predict the earliest possible phase for final performance prediction. The proposed technique was compared with the state-of-the-art machine learning (ML) classifiers, naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), simple logistic regression (SLR), repeated incremental pruning to produce error reduction (RIPPER), and sequential minimal optimization (SMO). The goal of this process is to predict the teams expected to obtain a below-average grade in software product development. The proposed technique outperforms others in the prediction of low performing teams at an early assessment stage. The proposed J48-based technique outperforms others by making 89% correct predictions. Full article
(This article belongs to the Special Issue Innovating Learning Analytics for Sustainable Higher Education)
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18 pages, 4237 KiB  
Article
Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques
by María Consuelo Sáiz Manzanares, Juan José Rodríguez Diez, Raúl Marticorena Sánchez, María José Zaparaín Yáñez and Rebeca Cerezo Menéndez
Sustainability 2020, 12(5), 1970; https://doi.org/10.3390/su12051970 - 05 Mar 2020
Cited by 16 | Viewed by 4432
Abstract
The use of learning environments that apply Advanced Learning Technologies (ALTs) and Self-Regulated Learning (SRL) is increasingly frequent. In this study, eye-tracking technology was used to analyze scan-path differences in a History of Art learning task. The study involved 36 participants (students versus [...] Read more.
The use of learning environments that apply Advanced Learning Technologies (ALTs) and Self-Regulated Learning (SRL) is increasingly frequent. In this study, eye-tracking technology was used to analyze scan-path differences in a History of Art learning task. The study involved 36 participants (students versus university teachers with and without previous knowledge). The scan-paths were registered during the viewing of video based on SRL. Subsequently, the participants were asked to solve a crossword puzzle, and relevant vs. non-relevant Areas of Interest (AOI) were defined. Conventional statistical techniques (ANCOVA) and data mining techniques (string-edit methods and k-means clustering) were applied. The former only detected differences for the crossword puzzle. However, the latter, with the Uniform Distance model, detected the participants with the most effective scan-path. The use of this technique successfully predicted 64.9% of the variance in learning results. The contribution of this study is to analyze the teaching–learning process with resources that allow a personalized response to each learner, understanding education as a right throughout life from a sustainable perspective. Full article
(This article belongs to the Special Issue Innovating Learning Analytics for Sustainable Higher Education)
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17 pages, 1406 KiB  
Article
A Predictive System Informed by Students’ Similar Behaviour
by Daniel Burgos
Sustainability 2020, 12(2), 706; https://doi.org/10.3390/su12020706 - 18 Jan 2020
Cited by 5 | Viewed by 1986
Abstract
It is quite complex to adapt instruction to student needs in view of online education owing to the ensuing communication disconnection in such learning environments. Decision support schemes offer assistance by automatically gathering students’ data and forwarding them to the tutor in the [...] Read more.
It is quite complex to adapt instruction to student needs in view of online education owing to the ensuing communication disconnection in such learning environments. Decision support schemes offer assistance by automatically gathering students’ data and forwarding them to the tutor in the appropriate perspective, in order to predict their behaviour and implement some action beforehand to avert or promote the final upshot. This study shows of a decision support scheme known as u-Tutor that is centred on the similarity computation between learners in the past, and how it was used in a real-case scenario. For this case study, this tool has been utilized by two real courses comprising of 392 learners alongside academic faculty, as of 2015 to 2019. The analysis offered focuses on 3 research areas: (1) perceived usefulness, (2) usability of the tool and (3) success rate of classification. From the acquired data, it can be seen that the teaching group managed to offer excellent approximations for those learners who eventually managed to pass the course, whereas u-Tutor seemed to be an early warning for learners at risk, indicating its capacity as a tutors’ supportive tool. Full article
(This article belongs to the Special Issue Innovating Learning Analytics for Sustainable Higher Education)
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