Learning Analytics for Academic Advising in Higher Education: Comparison
Please note this is a comparison between Version 2 by Lindsay Dong and Version 1 by Ibrahim ARPACI.

Learning analytics (LA) is a rapidly growing educational technology with the potential to enhance teaching methods and boost student learning and achievement. Despite its potential, the adoption of LA remains limited within the education ecosystem, and users who do employ LA often struggle to engage with it effectively. 

  • learning analytics
  • user intention
  • academic advising

1. Introduction

Recent technological developments have presented a chance to collect and monitor students’ learning patterns in digital environments, storing them as extensive sets of data [1]. Therefore, through learning analytics (LA), higher education in the twenty-first century persists in promoting discoveries in the field [2]. LA is commonly defined as measuring, storing, analyzing, and reporting data on students’ progress as well as the environments in which they learn, aiming to better comprehend and improve both the learning environment and learning itself [3]. Research has revealed several advantages of LA, such as the provision of customized course offerings, superior learning outcomes, curriculum design, teaching execution, and greater post-educational employment options [4].
Utilizing data gathered from learners’ interactions with a “Learning Management System” (LMS) to make predictions about the factors that contribute to increased student retention is one example of learning analytics. According to the findings of [5], students who are actively engaged by participating in more discussions, sending more messages, and completing more assessments tend to achieve greater overall grades in the course. The results highlight the potential role that learning analytics can play in guiding students’ academic success and growth [6].
Today’s instructors need a learning system that offers thorough and instructive LA for their online courses [7]. By accessing the LA of students’ lesson completion status and quiz results, educators may better understand students’ ability to follow and grasp the course contents, the themes they found problematic, their social relationships, and their knowledge gains [8]. LA has shown that it can detect learners’ unique requirements and learning challenges in addition to predicting student success. This knowledge might be used to build flexible educational structures with tailored directions for specific students [9]. Thus, when properly applied, LA could result in greater accountability at all educational levels.
The early promise of LA to enhance learning and its settings has not been fully realized [10]. There is currently little evidence demonstrating how LA services affect outcomes of student learning, processes of instruction and learning, as well as institutional decision-making [11]. Although numerous tools and methods for LA have been presented, there is little empirical evidence of the factors impacting this new technology’s potential adoption [8]. To handle the difficulties of LA adoption, previous researchers have developed numerous instruments and frameworks to guide the implementation of LA technologies in Australia, Europe, and North America [3]. Although those studies have enabled academics to pinpoint crucial factors that generally impact the LA services’ acceptance, there are still several complicated silos, competing leadership agendas, and institution-specific problems [12]. There have been few efforts to develop theoretical models and evaluate the variables determining the intention to utilize LA technologies [13].
The integration of learning analytics (LA) in the educational system is presently limited, and users often face challenges in effectively utilizing LA technologies [14]. This highlights a critical research problem: understanding and improving users‘ willingness to use LA dashboards, ultimately enabling smooth integration of LA tools in educational environments [15]. Learning analytics has substantial potential to revolutionize teaching methods and enhance student learning and success [16]. However, their underutilization and ineffective engagement hinder the realization of these advantages [17]. Addressing this research problem is crucial for optimizing the incorporation of LA tools, thereby enhancing educational methods and results.

2. Learning Analytics for Academic Advising in Higher Education

2.1. Learning Analytics Dashboards

Technology has evolved into a crucial tool that helps teachers and students create more effective learning environments over the past few years. The proliferation of online learning environments has substantially increased, expanding the amount of data created about the educational process [18]. Mitchell and Costello coined the term “Analytics of Learning” in 2000, presenting it as an emerging concept in their analysis of the potential prospects in the global market for the development and implementation of educational services via the network [19].
Stakeholders in higher education are increasingly employing “Learning Analytics” (LA) dashboards for various purposes, such as tools for learners to assess their progress [20], tools for administrators to support and control instructors, students, and staff [21], and tools for faculty to assess students’ performance and provide feedback on their teaching exercises [22].
LA is a field that focuses on gathering, analyzing, and sharing data related to learning environments and learners, evolved to realize the promise of this data analysis. LA is intended to address retention concerns and preserve the effectiveness of academic achievement [23]. LA was shown to be the most effective tool for growing awareness of the importance of bringing the ideas of information technologies and education closer together in the context of advancing higher education and, most significantly, learning professional and personal development based on the theoretical assumption of [5].
Numerous frameworks have been suggested to facilitate the adoption of LA and address the challenges it encounters. There are, however, few empirical studies on the variables impacting the potential adoption of LA technologies [8]. Klein et al. conducted qualitative research to comprehend organizational challenges, rewards, and opportunities relating to faculty and professional advising staff usage of LA techniques [24]. The results indicate that both organizational commitment and the organizational context, encompassing structures, processes, leadership, and policies, influence individuals’ choices to utilize and place trust in LA technologies. A study was performed [3] in European higher education organizations by interviewing institutional leaders. They identified context, challenges, strategy, and people as factors of LA adoption. Results suggested routine assessments of LA adoption to guarantee the desired changes and alignment of strategies.
Dawson et al. utilized “complexity leadership theory (CLT)” to identify the interaction between key dimensions of LA adoption. Interviews were conducted in Australian universities, and results suggested that to advance from the small-scale course stages to a more integrated and comprehensive organizational level, research on LA adoption models needs to extend [5]. Based on the UTAUT, Herodotou et al. analyzed the involvement patterns of university teachers using LA dashboards through in-depth interviews [25]. Results revealed that “social influence” (SI), “performance expectancy” (PE), and “effort expectancy” (EE) were among the elements promoting engagement with PLA. PE-facilitated conditions (FC) and a lack of knowledge of predictive data were factors that prevented PLA involvement. Based on the “technology acceptance model” (TAM), Ali et al. provided a model of the variables impacting instructors’ opinions on the use of LA tools [8]. The usage beliefs regarding an LA tool are related to the intention to employ the technology, according to the model. The study identified analytics categories that are the main interpreters of “perceived ease of use” and “perceived usefulness’ (interactive visualization).
Malaysia is known as one of the largest educational providers in Asia. The Malaysian government has consistently created efficient policies to improve the educational system. The newest but most crucial measure to enhance the educational system is thought to be the implementation of LA [23]. Malaysia is just beginning to investigate LA’s possibilities to aid student retention. The “Ministry of Higher Education” (MOHE) intends to emphasize LA to incorporate the learning and teaching transformation in higher education institutions, shifting the emphasis from retention to better satisfy the present changes in the industry known as the “fourth industrial revolution” (IR 4.0) [26]. To follow the IR 4.0 revolution, the MOHE advised a focus on four key areas: reforming learning classrooms, integrating 21st-century teaching methods, utilizing a flexible curriculum to address new developments and fields of knowledge, and utilizing the most recent teaching and learning technologies [27]. Studies confirmed academics’ high interest in utilizing LA for learning and teaching and its positive role in learners’ performance in Malaysian higher learning institutions [23,26][23][26]. Zaki et al. proposed an LA conceptual model in serious games for education in Malaysia [28]. Ismail et al. identified the LA as a powerful technique for exploring the data generated from the LMS and highlighted the likelihood of handling the LA technique with the LMS engagement in educational institutions [29]. However, LA implementation in Malaysia is fraught with difficulties. As a vital tool for the management and operation of educational institutions in Malaysia, LA is still not frequently utilized [27].

2.2. Applications of LADs in Educational Settings

“Learning Analytics Dashboards” (LADs) have evolved in sync with the advancements in educational technology and the growing abundance of educational data. While the roots of learning analytics can be traced back to the early 2000s, progress in data analytics, visualization techniques, and digital learning platforms has significantly impacted the creation and usage of LADs in higher education [30].
Learning analytics utilize methodologies from data science to analyze data and present the resulting analysis using diverse textual and visual approaches [31]. In the domain of LA, dashboards have gained significant attention as tools that can provide users with relevant insights, encourage self-reflection, and potentially guide interventions to optimize learning and improve the quality of the student experience [32]. As defined by Schwendimann et al., LADs are described as “a unified display that consolidates various indicators about the learner(s), learning process(es), and/or learning context(s) into one or multiple visual representations” [33] (p. 8).
These dashboards aggregate and synthesize diverse educational data, offering insights into student performance, engagement, behavior, and learning patterns [34]. LADs are designed to assist educators, administrators, and students in making informed decisions, optimizing teaching and learning strategies, and improving overall educational outcomes [35].
LADs provide a real-time view of individual and group academic performance [36]. Consequently, educators can monitor student progress, identify areas for improvement, and tailor instructional strategies accordingly. LADs analyze student data to craft personalized learning pathways based on individual weaknesses, strengths, and learning styles [37]. This facilitates a customized learning experience, enhancing student engagement and comprehension [38].
LADs can detect early signs of academic struggles or disengagement, enabling timely intervention and support for at-risk students [39]. This proactive approach can boost student retention rates [39]. Educators can utilize LADs to evaluate the effectiveness of courses and curricula [32]. Insights from the dashboards can guide adjustments to course content, assessments, and instructional strategies for better learning outcomes.
LADs can employ predictive models to forecast student success, aiding institutions in identifying students who may require additional support [40]. They evaluate student engagement and motivation levels using data on participation, interactions, and feedback [41]. This information empowers educators to implement strategies that enhance engagement and motivation [42]. Institutions can optimize resource allocation, including faculty time and support services, based on LAD insights [43]. This data-driven approach facilitates efficient planning and allocation of educational resources [33].
In summary, LADs are pivotal in contemporary higher education as they harness the power of data and analytics to improve teaching, enrich learning experiences, and promote student achievement [44]. They offer a multifaceted view of educational data, enabling stakeholders to make data-informed decisions and nurture a more effective and efficient educational ecosystem [42]. Integrating learning analytics into academic advising not only enhances the advising process but also contributes to student success and retention [39]. By harnessing the power of data analytics, academic advisors can provide timely, personalized guidance, ultimately supporting self-regulated learning [38].


  1. Banihashem, S.K.; Noroozi, O.; van Ginkel, S.; Macfadyen, L.P.; Biemans, H.J. A systematic review of the role of learning analytics in enhancing feedback practices in higher education. Educ. Res. Rev. 2022, 37, 100489.
  2. Nunn, S.; Avella, J.T.; Kanai, T.; Kebritchi, M. Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. Online Learn. 2016, 20, 13–29.
  3. Tsai, Y.-S.; Kovanović, V.; Gašević, D. Connecting the dots: An exploratory study on learning analytics adoption factors, experience, and priorities. Internet High. Educ. 2021, 50, 100794.
  4. Fan, S.; Chen, L.; Nair, M.; Garg, S.; Yeom, S.; Kregor, G.; Yang, Y.; Wang, Y. Revealing Impact Factors on Student Engagement: Learning Analytics Adoption in Online and Blended Courses in Higher Education. Educ. Sci. 2021, 11, 608.
  5. Dawson, S.; Poquet, O.; Colvin, C.; Rogers, T.; Pardo, A.; Gasevic, D. Rethinking Learning Analytics Adoption through Complexity Leadership Theory. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge, Sydney, Australia, 7–9 March 2018; ACM: New York, NY, USA, 2018; pp. 236–244.
  6. Kohnke, L.; Foung, D.; Chen, J. Using Learner Analytics to Explore the Potential Contribution of Multimodal Formative Assessment to Academic Success in Higher Education. SAGE Open 2022, 12.
  7. Herodotou, C.; Maguire, C.; Hlosta, M.; Mulholland, P. Predictive Learning Analytics and University Teachers: Usage and perceptions three years post implementation. In Proceedings of the LAK23: 13th International Learning Analytics and Knowledge Conference, Arlington, TX, USA, 13–17 March 2023; ACM: New York, NY, USA, 2023; pp. 68–78.
  8. Ali, L.; Asadi, M.; Gašević, D.; Jovanović, J.; Hatala, M. Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Comput. Educ. 2013, 62, 130–148.
  9. Lim, L.-A.; Dawson, S.; Gašević, D.; Joksimović, S.; Fudge, A.; Pardo, A.; Gentili, S. Students’ sense-making of personalised feedback based on learning analytics. Australas. J. Educ. Technol. 2020, 36, 15–33.
  10. Caspari-Sadeghi, S. Learning assessment in the age of big data: Learning analytics in higher education. Cogent Educ. 2023, 10, 2162697.
  11. Viberg, O.; Hatakka, M.; Bälter, O.; Mavroudi, A. The current landscape of learning analytics in higher education. Comput. Hum. Behav. 2018, 89, 98–110.
  12. Zilvinskis, J.; Willis, J.; Borden, V.M.H. An Overview of Learning Analytics. New Dir. High. Educ. 2017, 2017, 9–17.
  13. Alzahrani, A.S.; Tsai, Y.-S.; Iqbal, S.; Marcos, P.M.M.; Scheffel, M.; Drachsler, H.; Kloos, C.D.; Aljohani, N.; Gasevic, D. Untangling connections between challenges in the adoption of learning analytics in higher education. Educ. Inf. Technol. 2022, 28, 4563–4595.
  14. Sghir, N.; Adadi, A.; Lahmer, M. Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022). Educ. Inf. Technol. 2022, 28, 8299–8333.
  15. Gasevic, D.; Tsai, Y.-S.; Dawson, S.; Pardo, A. How do we start? An approach to learning analytics adoption in higher education. Int. J. Inf. Learn. Technol. 2019, 36, 342–353.
  16. Williamson, K.; Kizilcec, R. A Review of Learning Analytics Dashboard Research in Higher Education: Implications for Justice, Equity, Diversity, and Inclusion. In Proceedings of the LAK22: 12th International Learning Analytics and Knowledge Conference, Online, 21–25 March 2022; ACM: New York, NY, USA, 2022; pp. 260–270.
  17. Banihashem, K.; Macfadyen, L.P. Pedagogical Design: Bridging Learning Theory and Learning Analytics. Can. J. Learn. Technol. 2021, 47.
  18. Gaftandzhieva, S.; Docheva, M.; Doneva, R. A comprehensive approach to learning analytics in Bulgarian school education. Educ. Inf. Technol. 2021, 26, 145–163.
  19. Chatti, M.A.; Dyckhoff, A.L.; Schroeder, U.; Thüs, H. A reference model for learning analytics. Int. J. Technol. Enhanc. Learn. 2012, 4, 318–331.
  20. Bodily, R.; Verbert, K. Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems. IEEE Trans. Learn. Technol. 2017, 10, 405–418.
  21. Guerra, J.; Ortiz-Rojas, M.; Zúñiga-Prieto, M.A.; Scheihing, E.; Jiménez, A.; Broos, T.; De Laet, T.; Verbert, K. Adaptation and evaluation of a learning analytics dashboard to improve academic support at three Latin American universities. Br. J. Educ. Technol. 2020, 51, 973–1001.
  22. Brown, M. Seeing students at scale: How faculty in large lecture courses act upon learning analytics dashboard data. Teach. High. Educ. 2020, 25, 384–400.
  23. Kumar, S.R.; Hamid, S. Analysis of Learning Analytics in Higher Educational Institutions: A Review. In Proceedings of the Advances in Visual Informatics: 5th International Visual Informatics Conference, IVIC 2017, Bangi, Malaysia, 28–30 November 2017; pp. 185–196.
  24. Klein, C.; Lester, J.; Rangwala, H.; Johri, A. Learning Analytics Tools in Higher Education: Adoption at the Intersection of Institutional Commitment and Individual Action. Rev. High. Educ. 2019, 42, 565–593.
  25. Herodotou, C.; Maguire, C.; McDowell, N.; Hlosta, M.; Boroowa, A. The engagement of university teachers with predictive learning analytics. Comput. Educ. 2021, 173, 104285.
  26. West, D.; Tasir, Z.; Luzeckyj, A.; Na, K.S.; Toohey, D.; Abdullah, Z.; Searle, B.; Jumaat, N.F.; Price, R. Learning analytics experience among academics in Australia and Malaysia: A comparison. Australas. J. Educ. Technol. 2018, 34, 122–139.
  27. Mokhtar, S.; Alshboul, J.A.Q.; Shahin, G.O.A. Towards Data-driven Education with Learning Analytics for Educator 4.0. J. Phys. Conf. Ser. 2019, 1339, 012079.
  28. Zaki, N.A.A.; Zain, N.Z.M.; Noor, N.A.Z.M.; Hashim, H. Developing a Conceptual Model of Learning Analytics in Serious Games for STEM Education. J. Pendidik. IPA Indones. 2020, 9, 330–339.
  29. Ismail, S.N.; Hamid, S.; Ahmad, M.; Alaboudi, A.; Jhanjhi, N. Exploring Students Engagement Towards the Learning Management System (LMS) Using Learning Analytics. Comput. Syst. Sci. Eng. 2021, 37, 73–87.
  30. Mejia, C.; Florian, B.; Vatrapu, R.; Bull, S.; Gomez, S.; Fabregat, R. A Novel Web-Based Approach for Visualization and Inspection of Reading Difficulties on University Students. IEEE Trans. Learn. Technol. 2016, 10, 53–67.
  31. Jivet, I.; Scheffel, M.; Drachsler, H.; Specht, M. Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice. In Proceedings of the Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, 12–15 September 2017; pp. 82–96.
  32. Dunlosky, J.; Rawson, K.A.; Marsh, E.J.; Nathan, M.J.; Willingham, D.T. Improving Students’ Learning With Effective Learning Techniques. Psychol. Sci. Public Interes. 2013, 14, 4–58.
  33. Schwendimann, B.A.; Rodriguez-Triana, M.J.; Vozniuk, A.; Prieto, L.P.; Boroujeni, M.S.; Holzer, A.; Gillet, D.; Dillenbourg, P. Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research. IEEE Trans. Learn. Technol. 2016, 10, 30–41.
  34. Arnold, K.E.; Pistilli, M.D. Course Signals at Purdue: Using Learning Analytics to Increase Student Success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, BC, Canada, 29 April–2 May 2012; ACM: New York, NY, USA, 2012; pp. 267–270.
  35. Charleer, S.; Moere, A.V.; Klerkx, J.; Verbert, K.; De Laet, T. Learning Analytics Dashboards to Support Adviser-Student Dialogue. IEEE Trans. Learn. Technol. 2018, 11, 389–399.
  36. Beheshitha, S.S.; Hatala, M.; Gašević, D.; Joksimović, S. The Role of Achievement Goal Orientations When Studying Effect of Learning Analytics Visualizations. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge—LAK’16, Edinburgh, UK, 25–29 April 2016; ACM Press: New York, NY, USA, 2016; pp. 54–63.
  37. Shum, S.B.; Ferguson, R.; Martinez-Maldonado, R. Human-Centred Learning Analytics. J. Learn. Anal. 2019, 6.
  38. Matcha, W.; Uzir, N.A.; Gasevic, D.; Pardo, A. A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective. IEEE Trans. Learn. Technol. 2020, 13, 226–245.
  39. de Freitas, S.; Gibson, D.; Du Plessis, C.; Halloran, P.; Williams, E.; Ambrose, M.; Dunwell, I.; Arnab, S. Foundations of dynamic learning analytics: Using university student data to increase retention. Br. J. Educ. Technol. 2014, 46, 1175–1188.
  40. Verbert, K.; Duval, E.; Klerkx, J.; Govaerts, S.; Santos, J.L. Learning Analytics Dashboard Applications. Am. Behav. Sci. 2013, 57, 1500–1509.
  41. Brouwer, N.; Bredeweg, B.; Latour, S.; Berg, A.; van der Huizen, G. Learning Analytics Pilot with Coach2—Searching for Effective Mirroring. In Proceedings of the Adaptive and Adaptable Learning: 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Lyon, France, 13–16 September 2016; pp. 363–369.
  42. Few, S. Information Dashboard Design: The Effective Visual Communication of Data; O’Reilly Media: Sebastopol, CA, USA, 2006.
  43. Verbert, K.; Govaerts, S.; Duval, E.; Santos, J.L.; Van Assche, F.; Parra, G.; Klerkx, J. Learning dashboards: An overview and future research opportunities. Pers. Ubiquitous Comput. 2013, 18, 1499–1514.
  44. Gašević, D.; Kovanović, V.; Joksimović, S. Piecing the learning analytics puzzle: A consolidated model of a field of research and practice. Learn. Res. Pract. 2017, 3, 63–78.
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