Learning analytics in higher education --- challenges and policies: a review of eight learning analytics policies
Abstract
This paper presents the results of a review of eight policies for learning analytics of relevance for higher education, and discusses how these policies have tried to address prominent challenges in the adoption of learning analytics, as identified in the literature. The results show that more considerations need to be given to establishing communication channels among stakeholders and adopting pedagogy-based approaches to learning analytics. It also reveals the shortage of guidance for developing data literacy among end-users and evaluating the progress and impact of learning analytics. Moreover, the review highlights the need to establish formalised guidelines to monitor the soundness, effectiveness, and legitimacy of learning analytics. As interest in learning analytics among higher education institutions continues to grow, this review will provide insights into policy and strategic planning for the adoption of learning analytics.
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It is possible that stakeholders might not be aware of their textual data on digital platforms being used in LLMs-based innovations for different purposes of automation as the consenting process is often embedded into the enrollment or signing up of these platforms.
“It is possible that stakeholders might not be aware of their textual data (eg, forum posts or conversations) on digital platforms (eg, MOOCs and LMS) being used in LLMs-based innovations for different purposes of automation (eg, automated reply and training chatbots) as the consenting process is often embedded into the enrollment or signing up of these platforms (Tsai & Gasevic, 2017).”
From Practical and ethical challenges of large language models in education: A systematic scoping review (2024) · cites this paper as Tsai & Gasevic, 2017
Cites
Course signals at Purdue: using learning analytics to increase student success
Arnold, K. E., & Pistilli, M. D. (2012) · Proceedings of the 2nd International Conference on Learning Analytics and Knowledge · doi:10.1145/2330601.2330666 · Cited by 987 · see citing works
Drachsler, H., & Greller, W. (2016) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2883851.2883893 · Cited by 374 · see citing works
Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan
Macfadyen, L. P., & Dawson, S. (2012) · Cited by 284 · see citing works
Learning Analytics in Higher Education
Lester, J., Klein, C., Rangwala, H., & Johri, A. (2017) · ASHE Higher Education Report · doi:10.1002/aehe.20121 · Cited by 217 · see citing works
Macfadyen, L. P., Dawson, S., Pardo, A., & Gašević, D. (2014) · HAL · Cited by 147 · see citing works
Factors influencing beliefs for adoption of a learning analytics tool: An empirical study
Ali, L., Asadi, M., Gašević, D., Jovanović, J., & Hatala, M. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.10.023 · Cited by 135 · see citing works
Developing a Code of Practice for Learning Analytics
Sclater, N. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.31.3 · Cited by 133 · see citing works
Colvin, C., Rogers, T., Wade, A. T., Dawson, S., Gašević, D., Shum, S. B., Nelson, K. J., Alexander, S., Lockyer, L., Kennedy, G. E., et al. (2015) · UTS ePRESS (University of Technology Sydney) · Cited by 116 · see citing works
Setting Learning Analytics in Context: Overcoming the Barriers to Large-Scale Adoption
Ferguson, R., Macfadyen, L. P., Clow, D., Tynan, B., Alexander, S., & Dawson, S. (2014) · Journal of Learning Analytics · doi:10.18608/jla.2014.13.7 · Cited by 105 · see citing works
Code of practice for learning analytics
Sclater, N., & Bailey, P. M. (2015) · Cited by 74 · see citing works
An exercise in institutional reflection
Arnold, K. E., Lonn, S., & Pistilli, M. D. (2014) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2567574.2567621 · Cited by 55 · see citing works
Building organizational capacity for analytics
Norris, D. M., & Baer, L. L. (2012) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2330601.2330612 · Cited by 49 · see citing works
Building institutional capacities and competencies for systemic learning analytics initiatives
Arnold, K. E., Lynch, G., Huston, D. R., Wong, L., Jorn, L. A., & Olsen, C. W. (2014) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2567574.2567593 · Cited by 42 · see citing works
The learning analytics readiness instrument
Oster, M., Lonn, S., Pistilli, M. D., & Brown, M. G. (2016) · Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK '16 · doi:10.1145/2883851.2883925 · Cited by 31 · see citing works
Data literacy for learning analytics
Wolff, A., Moore, J., Zdráhal, Z., Hlosta, M., & Kužílek, J. (2016) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2883851.2883864 · Cited by 18 · see citing works
Crafting transformative strategies for personalized learning/analytics
Baer, L. L., Duin, A. H., Norris, D. M., & Brodnick, R. (2013) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2460296.2460354 · Cited by 11 · see citing works
Data Sharing for Learning Analytics - Questioning the Risks and Benefits
Hoel, T., Mason, J., & Chen, W. (2015) · International Conference on Computers in Education · Cited by 10 · see citing works
Learning Analytics in UK HE 2015: a HeLF survey report
Newland, B., Martin, L., & Ringan, N. (2015) · University of Brighton Repository (University of Brighton) · Cited by 7 · see citing works
HeLF Tablet Technologies in UK HE Survey Report, 2014
Newland, B., Ringan, N., & Martin, L. A. (2014) · University of Brighton Repository (University of Brighton) · Cited by 3 · see citing works
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Practical and ethical challenges of large language models in education: A systematic scoping review
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13370 · Cited by 935 · see citing works
“…conversations) on digital platforms (e.g., MOOCs and LMS) being used in LLMs-based innovations for different purposes of automation (e.g., automated reply and training chatbots) as the consenting process is often embedded into the enrollment or signing up of these platforms (Tsai and Gasevic, 2017).”
MethodNazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13232 · Cited by 589 · see citing works
“These phenomena might be attributed to the manifestation of fundamental human bias and heuristics of judgement under uncertainty (Tversky & Kahneman, 1974), such as Confirmation Bias and Belief Perseverance (Nickerson, 1998), and they can further lead teachers' resistance to the adoption of…”
BackgroundJin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100348 · Cited by 452 · see citing works
Large language models (LLMs): survey, technical frameworks, and future challenges
Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395 · see citing works
Barua, P. D., Vicnesh, J., Gururajan, R., Oh, S. L., Palmer, E., Azizan, M. M., Kadri, N. A., & Acharya, U. R. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031192 · Cited by 270 · see citing works
Knox, J., Williamson, B., & Bayne, S. (2020) · Learning Media and Technology · doi:10.1080/17439884.2019.1623251 · Cited by 261 · see citing works
“Learning analytics has garnered signi fi cant attention in education, following high-pro fi le research (see Lang et al. 2017) and policy agendas (see Tsai and Gasevic 2017), as well as consistent predictions of its impending disruption of formal education (for example Johnson et al. 2016; Adams…”
BackgroundRenz, A., & Hilbig, R. (2020) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-00193-3 · Cited by 218 · see citing works
“Tsai and Gasevic (2017) identify six general challenges — shortages of leadership, equal engagement, pedagogy-based approaches, sufficient training, studies empirically validating the influence by LA, and LA-specific policies — related to the strategic planning and implementation of LA in…”
BackgroundInfluentialCox, A. M. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-00237-8 · Cited by 242 · see citing works
“Echoing these arguments, Tsai et al.’s (2017, 2019) work captures why for all the hype around it, Learning Analytics have not yet found extensive practical application in HE.”
BackgroundLearning analytics in European higher education—Trends and barriers
Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Delgado Kloos, C., & Gašević, D. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103933 · Cited by 192 · see citing works
Self-citationComplexity leadership in learning analytics: Drivers, challenges and opportunities
Tsai, Y., Poquet, O., Gašević, D., Dawson, S., & Pardo, A. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12846 · Cited by 162 · see citing works
“This often results in unmatched expectations of LA (Tsai & Gašević, 2017) and the difficulty to form a shared vision or produce systematic outcomes.”
BackgroundMethodSelf-citationJivet, I., Scheffel, M., Schmitz, M., Robbers, S., Specht, M. M., & Drachsler, H. (2020) · The Internet and Higher Education · doi:10.1016/j.iheduc.2020.100758 · Cited by 172 · see citing works
Harnessing the Power of AI to Education
Srinivasa, K. G., Kurni, M., & Saritha, K. (2022) · Springer texts in education · doi:10.1007/978-981-19-6734-4_13 · Cited by 158 · see citing works
Jones, K. M. L., Asher, A., Goben, A. H., Perry, M. R., Salo, D., Briney, K. A., & Robertshaw, M. B. (2020) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24358 · Cited by 163 · see citing works
Tsai, Y., Perrotta, C., & Gašević, D. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2019.1676396 · Cited by 148 · see citing works
Self-citationLearning analytics in higher education: a preponderance of analytics but very little learning?
Guzmán-Valenzuela, C., Gómez-González, C., Rojas-Murphy Tagle, A., & Lorca-Vyhmeister, A. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00258-x · Cited by 131 · see citing works
“…central institutional units are in control of the processes of gathering and analysing the data with students and teachers being relegated to an observational role (Leitner et al. 2017; Tsai and Gasevic 2017) and having scant understandings of the LA techniques being put into use (Selwyn 2019).”
BackgroundUnpacking student engagement in higher education learning analytics: a systematic review
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00493-y · Cited by 138 · see citing works
Ethical issues in learning analytics: a review of the field
Tzimas, D., & Demetriadis, S. (2021) · Educational Technology Research and Development · doi:10.1007/s11423-021-09977-4 · Cited by 119 · see citing works
“In another review by Tsai and Gasevic (2017), the authors assessed 25 empirical studies, 38 desk studies, and eight LA policies.”
BackgroundYan, L., Martínez‐Maldonado, R., & Gašević, D. (2024) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3636555.3636856 · Cited by 154 · see citing works
“Such a combination has the potential to support systemic changes in education, making LA more comprehensive, evidence-based, and aligned with the real-world complexities of education [97].”
BackgroundHow do we start? An approach to learning analytics adoption in higher education
Gasevic, D., Tsai, Y.-S., Dawson, S., & Pardo, A. (2019) · The International Journal of Information and Learning Technology · doi:10.1108/ijilt-02-2019-0024 · Cited by 103 · see citing works
Self-citationPrieto, L. P., Rodríguez‐Triana, M. J., Martínez‐Maldonado, R., Dimitriadis, Y. A., & Gašević, D. (2018) · Australasian Journal of Educational Technology · doi:10.14742/ajet.4314 · Cited by 99 · see citing works
Learning analytics: state of the art
Hernández-de-Menéndez, M., Morales-Menendez, R., Escobar, C. A., & Ramírez Mendoza, R. A. (2022) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-022-00930-0 · Cited by 93 · see citing works
“In the United Kingdom, results are similar; from 53 organizations surveyed, 25 did not implement LA, 18 worked on it, nine partially implemented it, and only one fully implemented LA [68].”
BackgroundThe SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics
Tsai, Y.-S., Moreno-Marcos, P. M., Jivet, I., Scheffel, M., Tammets, K., Kollom, K., & Gašević, D. (2018) · Journal of Learning Analytics · doi:10.18608/jla.2018.53.2 · Cited by 93 · see citing works
Self-citationAnalysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics
Moreno-Marcos, P. M., Pong, T.-C., Muñoz‐Merino, P. J., & Kloos, C. D. (2020) · IEEE Access · doi:10.1109/access.2019.2963503 · Cited by 91 · see citing works
“However, it is important to note that there can also be limitations with data, both for the availability and access to them [7].”
BackgroundWorking Together in Learning Analytics Towards the Co-Creation of Value
Dollinger, M., Liu, D., Arthars, N., & Lodge, J. M. (2019) · Journal of Learning Analytics · doi:10.18608/jla.2019.62.2 · Cited by 90 · see citing works
“Frequently discussed already in LA literature, questions over how stakeholders feel about the use of their data, and ethical implications resulting in that use, often result in some wariness in users (Prinsloo & Slade, 2015; Tsai & Gašević, 2017).”
BackgroundLiao, C.-H., & Wu, J.-Y. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104599 · Cited by 89 · see citing works
Rethinking learning analytics adoption through complexity leadership theory
Dawson, S., Poquet, O., Colvin, C., Rogers, T., Pardo, A., & Gašević, D. (2018) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3170358.3170375 · Cited by 85 · see citing works
“In essence, there continues to be substantial challenges in addressing how LA can be effectively and efficiently embedded across an organization [26].”
Raffaghelli, J. E., & Stewart, B. E. (2020) · Teaching in Higher Education · doi:10.1080/13562517.2019.1696301 · Cited by 85 · see citing works
“Institutional policies addressing the use of Learning Analytics are still developing and more consideration has been shown to be needed in areas of pedagogy-based approach and establishment of communications channels among stakeholders (Tsai and Gasevic 2017).”
BackgroundInterpretable Multiview Early Warning System Adapted to Underrepresented Student Populations
Cano, A., & Leonard, J. D. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2911079 · Cited by 82 · see citing works
“The early warning system implemented at Purdue University is well known and simplifies performance alerts to traffic light colors [42], [43].”
BackgroundEducator perspectives on learning analytics in classroom practice
Shibani, A., Knight, S., & Buckingham Shum, S. (2020) · The Internet and Higher Education · doi:10.1016/j.iheduc.2020.100730 · Cited by 86 · see citing works
Raffaghelli, J. E., Manca, S., Stewart, B. E., Prinsloo, P., & Sangrà, A. (2020) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-00235-w · Cited by 90 · see citing works
“…reflections about the way students and teaching staff should engage with the academic and learning analytics systems yielded interesting considerations of the need for privacy by design, usability and engagement and transparency in students’ data usage (Jivet et al. 2020; Tsai and Gasevic 2017).”
BackgroundThe Student Expectations of Learning Analytics Questionnaire
Whitelock‐Wainwright, A., Gašević, D., Tejeiro, R., Tsai, Y., & Bennett, K. (2019) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12366 · Cited by 63 · see citing works
“The challenge of unequal stakeholder engagement in LA implementations is not limited to U.K. higher education institutions (Tsai & Ga š evi ć , 2017a), and it is necessary for each university that is interested in implementing LA services to actively engage with their stakeholders.”
InfluentialSelf-citationEquality of Learning Opportunity via Individual Fairness in Personalized Recommendations
Marras, M., Boratto, L., Ramos, G., & Fenu, G. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00271-1 · Cited by 62 · see citing works
“Although the community has been interested in the ethical dimensions of data-driven educational systems Drachsler et al. (2015); Sclater and Bailey (2015); Tsai and Gasevic (2017), the focus has often been on policies.”
BackgroundBhimdiwala, A., Neri, R. C., & Gomez, L. M. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00278-8 · Cited by 64 · see citing works
“(2) Teachers find the technology a true representation of and complement to their teaching identity (Pardo & Siemens, 2014; Tsai & Gašević, 2017).”
BackgroundTsai, Y., Moreno-Marcos, P. M., Tammets, K., Kollom, K., & Gašević, D. (2018) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3170358.3170367 · Cited by 55 · see citing works
“The third main issue has been highlighted in a systematic literature review where Tsai and Gašević pointed out that HEIs struggle to find common grounds among different stakeholders regarding the adoption of LA, due to discrepancies in existing experience and knowledge of data, therefore resulting in different underst…”
BackgroundSelf-citationLearning Analytics Challenges to Overcome in Higher Education Institutions
Leitner, P., Ebner, M., & Ebner, M. (2019) · Springer eBooks · doi:10.1007/978-3-319-64792-0_6 · Cited by 54 · see citing works
Intelligent techniques in e-learning: a literature review
Ilić, M., Mikić, V., Kopanja, L., & Vesin, B. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10508-1 · Cited by 50 · see citing works
“In addition, available data often cannot capture or present a complete picture of a learning process (Tsai and Gasevic 2017), while obtaining and integrating data from various sources represents a major challenge (Tsai et al. 2021).”
BackgroundAn exploratory latent class analysis of student expectations towards learning analytics services
Whitelock‐Wainwright, A., Tsai, Y., Drachsler, H., Scheffel, M., & Gašević, D. (2021) · The Internet and Higher Education · doi:10.1016/j.iheduc.2021.100818 · Cited by 48 · see citing works
Self-citationKollom, K., Tammets, K., Scheffel, M., Tsai, Y., Jivet, I., Muñoz‐Merino, P. J., Moreno-Marcos, P. M., Whitelock‐Wainwright, A., Calleja, A. R., Gašević, D., et al. (2020) · The Internet and Higher Education · doi:10.1016/j.iheduc.2020.100788 · Cited by 46 · see citing works
Self-citationRaffaghelli, J. E. (2020) · Education Sciences · doi:10.3390/educsci10090233 · Cited by 61 · see citing works
Paris, B. S., Reynolds, R. B., & McGowan, C. (2021) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24575 · Cited by 44 · see citing works
“…review terms of service and contracts with vendors to ensure that the university advocates for data policy that serves the educational mission of the institution and includes input from the broad swath of university stakeholders with significant transparency (Borgman, 2018; Tsai & Gasevic, 2017).”
BackgroundPozdniakov, S., Martinez-Maldonado, R., Tsai, Y.-S., Echeverria, V., Srivastava, N., & Gasevic, D. (2023) · LAK23: 13th International Learning Analytics and Knowledge Conference · doi:10.1145/3576050.3576063 · Cited by 44 · see citing works
“either insufficiently accurate [21, 39] or require specialised skills or training to use them [21, 46].”
BackgroundSelf-citationAssessing the validity of a learning analytics expectation instrument: A multinational study
Whitelock‐Wainwright, A., Gašević, D., Tsai, Y., Drachsler, H., Scheffel, M., Muñoz‐Merino, P. J., Tammets, K., & Kloos, C. D. (2020) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12401 · Cited by 43 · see citing works
Self-citationClark, J.-A., Liu, Y., & Isaias, P. T. (2020) · Australasian Journal of Educational Technology · doi:10.14742/ajet.6164 · Cited by 44 · see citing works
“…2019), questionnaire surveys (Arnold, Lonn, et al., 2014; Norris & Baer, 2013; Oster et al., 2016; Saint & Gutierrez, 2017; West et al., 2018), and case and policy studies (Arnold, Lynch, et al., 2014; Lonn et al., 2017; Tsai & Gašević, 2017), mostly done at a single institution in one country.”
BackgroundUntangling connections between challenges in the adoption of learning analytics in higher education
Alzahrani, A. S., Tsai, Y., Iqbal, S., Moreno-Marcos, P. M., Scheffel, M., Drachsler, H., Kloos, C. D., Aljohani, N. R., & Gašević, D. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11323-x · Cited by 39 · see citing works
“…have a chain of negative impacts on an institution’s overall capacity for LA. Stakeholder involvement and continued participation have been vital for the success of LA projects (Ferguson et al., 2014; Greller & Drachsler, 2012; Lester et al., 2017; Tsai & Gasevic, 2017; Tsai et al., 2019, 2020).”
BackgroundMethodInfluentialSelf-citationLearning Analytics to Support Teaching Skills: A Systematic Literature Review
Oliva-Córdova, L. M., García‐Cabot, A., & Amado-Salvatierra, H. R. (2021) · IEEE Access · doi:10.1109/access.2021.3070294 · Cited by 39 · see citing works
“Further considerations are needed to establish communication channels between interested parties and adopt peda-gogically based approaches to LA [38].”
BackgroundÇelik, İ., Gedrimiene, E., Silvola, A., & Muukkonen, H. (2022) · Policy Futures in Education · doi:10.1177/14782103221078401 · Cited by 39 · see citing works
“Previous review studies indicated opportunities of LA in higher education (e.g., Viberg et al., 2018; Tsai and Gasevic, 2017).”
BackgroundLearning Analytics Based on Wearable Devices: A Systematic Literature Review From 2011 to 2021
Liu, Z., Ren, Y., Kong, X., & Liu, S. (2022) · Journal of Educational Computing Research · doi:10.1177/07356331211064780 · Cited by 38 · see citing works
“Furthermore, the adoption of learning analytics in large scale organizations comes with challenges, and it urgently requires new leadership patterns, collaborative approaches, policymaking, and strategy planning to discipline relevant research (Tsai & Gasevic, 2017).”
BackgroundMore Than Figures on Your Laptop: (Dis)trustful Implementation of Learning Analytics
Tsai, Y., Whitelock‐Wainwright, A., & Gašević, D. (2021) · Journal of Learning Analytics · doi:10.18608/jla.2021.7379 · Cited by 35 · see citing works
Self-citationReflections and Challenges in Networked Learning
Dohn, N. B., Sime, J., Cranmer, S., Ryberg, T., & de Laat, M. F. (2018) · Springer eBooks · doi:10.1007/978-3-319-74857-3_11 · Cited by 34 · see citing works
Tsai, Y., Kovanović, V., & Gašević, D. (2021) · The Internet and Higher Education · doi:10.1016/j.iheduc.2021.100794 · Cited by 34 · see citing works
Self-citationVisualization of education blockchain data: trends and challenges
Ocheja, P., Flanagan, B., Ogata, H., & Oyelere, S. S. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2022.2026406 · Cited by 34 · see citing works
“One could argue that this is so because the use of blockchain in the fi eld of education is still new and even the concept of learning analytics su ff ers low adoption (Macfadyen et al., 2014; Tsai & Gasevic, 2017).”
BackgroundA social cartography of analytics in education as performative politics
Prinsloo, P. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12872 · Cited by 35 · see citing works
Four paradigms in learning analytics: Why paradigm convergence matters
Baker, R. S., Gašević, D., & Karumbaiah, S. (2021) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2021.100021 · Cited by 32 · see citing works
Rets, I., Herodotou, C., & Gillespie, A. (2023) · Journal of Learning Analytics · doi:10.18608/jla.2023.7743 · Cited by 31 · see citing works
“For example, Tsai and Gašević (2017) compared eight existing policies for LA and found that communications between relevant stakeholders in the reviewed policies are top-down, limited to the aspects of what, how, and why data are collected; none of the policies mentioned a review and evaluation of…”
Who is Helping Whom? Student Concerns about AI-Teacher Collaboration in Higher Education Classrooms
Han, B., Coghlan, S., Buchanan, G., & McKay, D. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711104 · Cited by 31 · see citing works
“ is gap is acknowledged by SHEILA’s architects, with Tsai et al. [140] critiquing the limited research on the impact of technology adoption on the student experience and learning journey.”
Ethical and Pedagogical Impacts of AI in Education
Han, B., Nawaz, S., Buchanan, G. R., & McKay, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36272-9_54 · Cited by 30 · see citing works
Context Matters: Differing Implications of Motivation and Help-Seeking in Educational Technology
Karumbaiah, S., Ocumpaugh, J. L., & Baker, R. S. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00272-0 · Cited by 30 · see citing works
“The current literature on ethics in the field has mostly been interested in issues of data ownership and privacy and institutional and policy level considerations (Draschler et al., 2015; Tsai & Gasevic, 2017).”
Toward an Institutional Analytics Agenda for Addressing Student Dropout in Higher Education
Silva, L. M. H. D., Chounta, I., Rodríguez‐Triana, M. J., Roldan-Roa, E., Gramberg, A., & Valk, A. (2022) · Journal of Learning Analytics · doi:10.18608/jla.2022.7507 · Cited by 48 · see citing works
“Thus, exploring and documenting stakeholder perceptions is essential in order to increase the efficacy of IA solutions (Falc˜ao et al., 2020; Dollinger et al., 2019; Tsai & Gasevic, 2017).”
Al-Rikabi, Y. K., & Montazer, G. A. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11889-0 · Cited by 28 · see citing works
Perceptions and expectations about learning analytics from a brazilian higher education institution
Falcão, T. P., Mello, R. F., Rodrigues, R. L., Diniz, J. R. B., Tsai, Y., & Gašević, D. (2020) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3375462.3375478 · Cited by 26 · see citing works
Self-citationStudents' expectations of Learning Analytics across Europe
Wollny, S., Mitri, D. D., Jivet, I., Muñoz‐Merino, P. J., Scheffel, M., Schneider, J., Tsai, Y., Whitelock‐Wainwright, A., Gašević, D., & Drachsler, H. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12802 · Cited by 24 · see citing works
“…student stakeholder engagement owing to what has been identified as an oversight when creating LA services: the inclusion of the student perspective both in the development of institutional implementation approaches (Tsai & Gasevic, 2017) and the design of LA tools (Buckingham Shum et al., 2019).”
Self-citationKaliisa, R., Mørch, A. I., & Kluge, A. (2021) · Technology Knowledge and Learning · doi:10.1007/s10758-020-09488-w · Cited by 25 · see citing works
Analítica del aprendizaje y educación basada en datos: Un campo en expansión
Figaredo, D. D., Reich, J., & Ruipérez‐Valiente, J. A. (2020) · RIED Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.23.2.27105 · Cited by 23 · see citing works
“Al igual que sucede con otras mejoras, el objetivo final sería que esas aplicaciones puedan integrarse de forma sistemática dentro de la infraestructura de las instituciones educativas, lo que a su vez plantea una serie de desafíos nuevos (Tsai y Gasevic, 2017).”
BackgroundLearning Analytics in Education, Advantages and Issues: A Systematic Literature Review
Mian, Y. S., Khalid, F. B., Qun, A. W. C., & Ismail, S. S. (2022) · Creative Education · doi:10.4236/ce.2022.139183 · Cited by 22 · see citing works
“This is due to the fact that even though higher education institutions have taken their own initiative to practice learning analytics, it is still in the preparation or early stage (Tsai & Gasevic, 2017).”
BackgroundClinkard, K. (2018) · Industry and Higher Education · doi:10.1177/0950422218808625 · Cited by 21 · see citing works
“…metrics refer to a single point of reference, they lack sophistication and sensitivity to other related valuable gains from a university experience, which may be found in student records, CVs and portfolios, as well as in analyses of metadata from institutional systems (Tsai and Gasevic, 2017).”
BackgroundFlanagan, B., Majumdar, R., & Ogata, H. (2022) · IEEE Access · doi:10.1109/access.2022.3156073 · Cited by 20 · see citing works
Vigentini, L., Liu, D. Y. T., Arthars, N., & Dollinger, M. (2020) · The Internet and Higher Education · doi:10.1016/j.iheduc.2020.100728 · Cited by 20 · see citing works
Predictive Learning Analytics in Higher Education: Factors, Methods and Challenges
Al-Tameemi, G., Xue, J. W. J., Ajit, S., Kanakis, T., & Hadi, I. (2020) · International Conference on Advances in Computing and Communication Engineering · doi:10.1109/icacce49060.2020.9154946 · Cited by 20 · see citing works
Curriculum analytics in higher education institutions: a systematic literature review
Silva, L. M. H. D., Rodríguez‐Triana, M. J., Chounta, I., & Pishtari, G. (2024) · Journal of Computing in Higher Education · doi:10.1007/s12528-024-09410-8 · Cited by 19 · see citing works
“As already stressed in the LA community, ethical and privacy issues are challenging and highly sensitive topics when talking about educational datasets (Tsai & Gasevic, 2017) that should be tackled by default, and papers should report on their compliance with applying policies and guidelines.”
A learning analytics journey: Bridging the gap between technology services and the academic need
Munguia, P., Brennan, A., Taylor, S., & Lee, D. (2020) · The Internet and Higher Education · doi:10.1016/j.iheduc.2020.100744 · Cited by 19 · see citing works
Heiser, R., Dello Stritto, M. E., Brown, A., & Croft, B. (2023) · Journal of Learning Analytics · doi:10.18608/jla.2023.7775 · Cited by 18 · see citing works
Shankar, S. K., Rodríguez‐Triana, M. J., Ruiz‐Calleja, A., Prieto, L. P., Chejara, P., & Martínez‐Monés, A. (2020) · IEEE Revista Iberoamericana de Tecnologias del Aprendizaje · doi:10.1109/rita.2020.2987887 · Cited by 18 · see citing works
“3 In fact, we currently count on much more mature conceptual tools for researchers, teachers, software developers and institutional leaders [27], [28] on how to design and enact LA projects.”
BackgroundData in practice: A participatory approach to understanding pre-service teachers’ perspectives
Prestigiacomo, R., Hunter, J. L., Knight, S., Martínez‐Maldonado, R., & Lockyer, L. (2020) · Australasian Journal of Educational Technology · doi:10.14742/ajet.6388 · Cited by 17 · see citing works
“Yet, to effectively design learning with LA, teachers require a degree of data literacy (Gummer & Mandinach, 2015; Tsai & Gasevic, 2017; Wasson & Hansen, 2015).”
BackgroundTaşçı, S., & Tunaz, M. (2024) · Araştırma ve Deneyim Dergisi · doi:10.47214/adeder.1575897 · Cited by 17 · see citing works
Policy Matters: Expert Recommendations for Learning Analytics Policy
Scheffel, M., Tsai, Y.-S., Gašević, D., & Drachsler, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29736-7_38 · Cited by 16 · see citing works
“This is revealed in a study by Tsai and Gašević [42].”
BackgroundResultInfluentialSelf-citationApplications of learning analytics in Latin America
Pontual Falcão, T., Ferreira Mello, R., & Lins Rodrigues, R. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.12978 · Cited by 16 · see citing works
Using online student interactions to predict performance in a first-year computing science course
Goundar, S., Deb, A., Lal, G., & Naseem, M. (2022) · Technology Pedagogy and Education · doi:10.1080/1475939x.2021.2021977 · Cited by 16 · see citing works
“As interest in learning analytics among higher education institutions grows, Tsai and Gasevic (2017) evaluated learning analytics policies and recommended adopting pedagogy-based approaches to learning analytics.”
BackgroundFoLA2 — A Method for Co-creating Learning Analytics–Supported Learning Design
Schmitz, M., Scheffel, M., Bemelmans, R., & Drachsler, H. (2022) · Journal of Learning Analytics · doi:10.18608/jla.2022.7643 · Cited by 15 · see citing works
“Several scholars have concluded that although institutions are aware of the possibilities of analyzing learning data, only a few go beyond counting the number of clicks in an online learning environment (Colvin et al., 2016; Haythornthwaite, 2017; Tsai & Gasevic, 2017).”
BackgroundEl Proceso de Implementación de Analíticas de Aprendizaje
Ruipérez‐Valiente, J. A. (2020) · RIED Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.23.2.26283 · Cited by 15 · see citing works
“…aprendizaje de forma sistemática (Arnold et al., 2014), así como para resaltar cuáles son los principales desafíos que las instituciones afrontan (Tsai y Gasevic, 2017), como por ejemplo los programas de reciclaje profesional e incentivos hacia educadores para conseguir que empiecen a adquirir…”
Yürüm, O. R. (2025) · IEEE Access · doi:10.1109/access.2025.3572467 · Cited by 16 · see citing works
“Additionally, the literature includes in-depth explorations of ethical considerations, trends, and policy implications, which can inform decision-making processes within higher education and educational technology fields [30], [31].”
A Review on the Role of Artificial Intelligence in Personalized Learning
Qureshi, R., Hajare, S., & Verma, P. (2024) · Asia Pacific Conference on Innovation in Technology (APCIT) · doi:10.1109/apcit62007.2024.10673706 · Cited by 14 · see citing works
“L ITERATURE REVIEW An overview of eight learning analytics policies relevant to higher education is given accompanied by a discussion of how these policies have attempted to solve key problems in the adoption of learning analytics as pointed out in the literature [5, 6].”
BackgroundGraduate Education in China Meets AI: Key Factors for Adopting AI-Generated Content Tools
Tang, Y., & Su, L. (2024) · Libri · doi:10.1515/libri-2024-0079 · Cited by 16 · see citing works
Hakami, E., & Hernández‐Leo, D. (2020) · Repositori digital de la UPF (Universitat Pompeu Fabra) · Cited by 13 · see citing works
Students' Perceptions about Learning Analytics in a Brazilian Higher Education Institution
Falcão, T. P., Ferreira, R., Rodrigues, R. L., Diniz, J. R. B., & Gašević, D. (2019) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2019.00049 · Cited by 14 · see citing works
“To deal with this issue, several institutions started to elaborate their own policies on LA [8].”
BackgroundPrivacy-Driven Learning Analytics
Joksimovíc, S., Marshall, R., Rakotoarivelo, T., Ladjal, D., Zhan, C., & Pardo, A. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-3-030-86316-6_1 · Cited by 14 · see citing works
A Penny for your Thoughts: Students and Instructors’ Expectations about Learning Analytics in Brazil
Falcão, T. P., Rodrigues, R. L., Cechinel, C., Dermeval, D., Oliveira, E., Gasparini, I., Araújo, R. D., Primo, T. T., Gašević, D., & Mello, R. F. (2022) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3506860.3506886 · Cited by 13 · see citing works
A systems perspective on data and analytics for distance education
Ifenthaler, D. (2022) · Distance Education · doi:10.1080/01587919.2022.2064828 · Cited by 13 · see citing works
Towards a Maturity Model for Learning Analytics Adoption An Overview of its Levels and Areas
Freitas, E., de Souza, F. D. F., Garcia, V. C., Ferreira, R., & Gašević, D. (2020) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt49669.2020.00059 · Cited by 13 · see citing works
“Furthermore, “it is of great importance that institutions adopt learning analytics under clear guidelines that are grounded in their cultural, social, economic and political contexts and are based on existing best practices for learning analytics and learning theo-ries” [6].”
BackgroundPozdniakov, S., Martínez‐Maldonado, R., Tsai, Y., Srivastava, N., Liu, Y., & Gašević, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42682-7_23 · Cited by 12 · see citing works
Self-citationMMALA: Developing and Evaluating a Maturity Model for Adopting Learning Analytics
Freitas, E., de Souza, F. D. F., Garcia, V. C., Falcão, T. P., Marques, E. C., Gašević, D., & Mello, R. F. (2024) · Journal of Learning Analytics · doi:10.18608/jla.2024.8099 · Cited by 12 · see citing works
Charting Design Needs and Strategic Approaches for Academic Analytics Systems through Co-Design
Tsai, Y., Singh, S., Raković, M., Lim, L.-A., Roychoudhury, A., & Gašević, D. (2022) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3506860.3506939 · Cited by 12 · see citing works
Self-citationLearning Analytics: Pathways to Impact
Corrin, L., Scheffel, M., & Gašević, D. (2020) · Australasian Journal of Educational Technology · doi:10.14742/ajet.6853 · Cited by 12 · see citing works
“Impact of the institutional implementation of learning analytics When implementing learning analytics systems within an institution there are a range of factors that need to be addressed for the system to have impact (Macfadyen et al., 2014; Tsai & Gašević, 2017).”
BackgroundWang, M., Chen, Z., Xu, Y., Maheshi, B., & Gašević, D. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00538-w · Cited by 18 · see citing works
“(1) For teacher education institutions, strengthen training in data literacy (Shreiner & Dykes, 2021; Tsai & Gašević,2017) among pre-service teachers to ensure they can understand and interpret the findings of intelligent teaching analytics, fostering evidence-based reflection and making…”
Preserving Both Privacy and Utility in Learning Analytics
Zhan, C., Joksimovíc, S., Ladjal, D., Rakotoarivelo, T., Marshall, R., & Pardo, A. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3393766 · Cited by 14 · see citing works
“In so doing, they [25] investigated the four dimensions of LA research, namely strategy, legal/organizational obligations, privacy protection, and data management and governance, and analyzed how each dimension has been addressed in existing policies.”
BackgroundMpofu, S., & Chasokela, D. (2024) · Advances in educational marketing, administration, and leadership book series · doi:10.4018/979-8-3693-6967-8.ch004 · Cited by 11 · see citing works
Ortíz-Rojas, M., Maya, R., Jiménez, A., Hilliger, I., & Chiluiza, K. (2019) · Latin American Conference on Learning Technologies (LACLO) · doi:10.1109/laclo49268.2019.00029 · Cited by 10 · see citing works
“Not mentioning the specific methodology is acceptable in Anglo Saxon countries where there are already some initiatives and even policies for the adoption of LA [10].”
MethodSurveillance in the System: Data as Critical Change in Higher Education
Szcyrek, S., & Stewart, B. E. (2022) · The Open/Technology in Education Society and Scholarship Association Journal · doi:10.18357/otessaj.2022.2.2.34 · Cited by 9 · see citing works
Linking Assessment and Learning Analytics to Support Learning Processes in Higher Education
Schumacher, C. (2019) · Springer eBooks · doi:10.1007/978-3-319-17727-4_166-1 · Cited by 9 · see citing works
DSLE: A Smart Platform for Designing Data Science Competitions
Attanasio, G., Giobergia, F., Pasini, A., Ventura, F., Baralis, E., Cagliero, L., Garza, P., Apiletti, D., Cerquitelli, T., & Chiusano, S. (2020) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac48688.2020.00026 · Cited by 9 · see citing works
“Over the last decade, the Learning Analytics (LA) community has carried out a number of research projects tailored to higher-level courses [8].”
Galici, R., Käser, T., Fenu, G., & Marras, M. (2023) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3576050.3576148 · Cited by 9 · see citing works
“Trust is essential for the acceptance of these models in education [18, 19, 29].”
Background
- Authors
- Tsai, Y.-S., & Gasevic, D.
- Affiliations
- University of Edinburgh (GB)
- The University of Edinburgh, Edinburgh, United Kingdom
- ORCID
- Tsai, Yi-Shan
- Venue
- Proceedings of the Seventh International Learning Analytics & Knowledge Conference
- Publisher
- ACM
- Published
- 2017-03-13
- Pages
- 233-242
- OpenAlex
- W2593639882
- Cited by
- 211
- References
- 20
- Citations by year
- 2026: 20 · 2025: 22 · 2024: 32 · 2023: 31 · 2022: 28 · 2021: 20 · 2020: 31 · 2019: 14 · 2018: 10 · 2017: 3
- FWCI
- 14.63
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- HAL (submittedVersion)
- Funders
- European Commission (562080-EPP-1-2015-1-BE-EPPKA3-PIFORW)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Computer Science › Information Systems › Educational and Technological Research
- Keywords
- learning analytics, higher education, barriers to adoption, stakeholder communication, data literacy, strategic planning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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