RESEARCH

 

BENEFITS OF LA RESEARCH

LA provides insights into students’ learning processes and gaps in understanding. This can help teachers recognise weaknesses in learning activities and challenging topics, as well as provide constructive feedback that can enhance students’ further learning (Gašević et al., 2015). As for students, LA plays a significant role in fostering self-regulated learning (Schumacher & Ifenthaler, 2018) as an active process in which students set their learning goals and then “monitor, regulate, and control their cognition, motivation, and behaviour” (Pintrich, 2000, p. 453). An area of particular research interest has recently been related to assessment analytics (Nguyen et al., 2017; Divjak & Maretić, 2017) In higher education, analytics often aims at early identification of students at risk, as a basis for interventions aimed at maximizing student retention, and enhancing the quality of students’ learning experiences (Susnjak et al., 2022).

Some of the key goals of LA include providing students with personalised and timely feedback on their learning, and supporting awareness through self-reflection; supporting the quality of learning and teaching with evidence on the success of innovations; supporting students in development of lifelong learning skills and strategies, as well as skills like critical thinking, collaboration, creativity, communication (SOLAR, n. d.).

LA can have different levels of complexity, supporting informed decision-making by providing several layers of insights ( Susnjak et al., 2022; SOLAR, n. d.) :

  •  descriptive analytics provides basic information about trends and current status

  •  diagnostic analytics looks into why something happened

  •  predictive analytics provides forecasts and estimates future outcomes (based on past and current patterns)

  •  prescriptive analytics produces tailored recommendations and suggestions for behavioural changes which could lead to positive outcomes.

 

research
 
From: Divjak et al. (2023).  Assessment validity and learning analytics as prerequisites for ensuring student‐centred learning design. British Journal of Educational Technology.
 

LAB'S RESEARCH PLAN

The LA Lab's team continuously and closely follows the developments related to LA research, which is reflected in our research plan. Our most current research endeavors are primarily related to learning design and design analytics, assessment analytics, developing predictive LA models, and the trustworthiness of LA and AI in education. However, our research plan is much broader, and includes a range of related topics.

Learning design & design analytics
Learning design describes the teaching and learning activities leading to the acquisition of intended learning outcomes. Design analytics help the development and upgrading of learning design that aligns with learning outcomes and student-centered, innovative pedagogies. Learning design and LA are interconnected: while sound learning design is essential for meaningful LA, insights from LA present a valuable basis for upgrading learning design.
LA and assessment
LA provides an important input in the development and improvement of assessment programs, including their validity and reliability. Different kinds of data can used for analyses, like assessment data and trace data from LMSs, but also multimodal data.
Trustworthiness of LA
Trustworthiness of LA, closely related to the trustworthiness of AI, has been gaining more focus in the LA community lately. The research focuses on various aspects, such as privacy and data protection, agency, data quality, algorithms, infrastructure, transparency, and other.
Predictive LA models
Predictive LA uses past and present data patterns to predict outcomes in the future, usually using machine learning algorithms. It uses different kinds of data, including students' academic data, as well as behavioral, demographic and other data.
Learning Analytics Design Patterns
Appropriate visualizations could make a significant contribution to understanding the large amounts of educational data. Statistical, filtering, and mining tools should be designed in a way that can help learners, teachers, and institutions to achieve their analytics objectives. Educational data mining tools should be designed for non-expert users in data mining
Learning Analytics Evaluation
Investigate how LA could impact learning and how this could be evaluated. Further research is required to investigate effective mixed-method evaluation approaches that do focus on the usability of the LA tool, but also on aim at measuring its impact on learning.
Context Modelling, Personalized Learning Analytics
What sort of indicators and metrics are helpful in each learning situation. LA tools should support an exploratory, real-time user experience. From a technical perspective, templates and rule engines can be used to achieve goal-oriented LA for improved personalized learning

POSTERS 

 

References:

  •  Divjak, B., & Maretić, M. (2017). Learning Analytics for Peer-assessment. Journal of Information and Organizational Sciences, 41(1), 21–34. https://doi.org/10.31341/jios.41.1.2
  •  Divjak, B., Svetec, B., Horvat, D., & Kadoić, N. (2023b). Assessment validity and learning analytics as prerequisites for ensuring student‐centred learning design. British Journal of Educational Technology, 54(1), 313–334. https://doi.org/10.1111/bjet.13290
  •  Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x
  •  Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., & Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, 76, 703–714. https://doi.org/10.1016/j.chb.2017.03.028
  •  Pintrich, P. R. (2000). The Role of Goal Orientation in Self-Regulated Learning. In Handbook of Self-Regulation (pp. 451–502). Elsevier. https://doi.org/10.1016/B978-012109890-2/50043-3
  •  Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in Human Behavior, 78, 397–407. https://doi.org/10.1016/j.chb.2017.06.030
  •  Society for Learning Analytics Research. (n.d.). What is learning analytics? Retreived on 7 April 2022 from https://www.solaresearch.org/about/what-is-learning-analytics/
  •  Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: a tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 12. https://doi.org/10.1186/s41239-021-00313-7

LA Lab

LA Lab operates under the Faculty of Organization and Informatics (FOI) in Varaždin (UniZG), Croatia.

Contact us

  Full Prof. Blaženka Divjak, PhD
 blazenka.divjak@foi.unizg.hr
  +385 42 390 842

About FOI

FOI is one of the constituent units of the University of Zagreb. It is located in the northern part of Croatia, in the very heart of the beautiful baroque City of Varaždin.

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