Exploring the Learning Analytics Equation - What About the Carpe Diem of Teaching and Learning?

Oct 15, 2026

Uploaded by Aran

Humans are attracted to using data for understanding phenomena and making predictions, and learning analytics promises to understand and optimize learning and the learning environments through data collected from learners and contexts. This study systematically reviews research using bibliometric, data mining, and analytics methods and finds rising interest, country and institutional agendas, and a need for interdisciplinary work blending technology with pedagogy. Text mining and social network analysis identify seven research themes, and the paper emphasizes ethics, privacy, learner-centered social values, and risks of educational panopticism and quantified decisions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Key themes identified by the study

Introduction: Data-Driven, Digitally-Shaped Educational Landscapes

The State of the Art in Learning Analytics Research

Exploring the Learning Analytics Equation: What About the Carpe Diem of Teaching and Learning? Aras Bozkurt, Ramesh C Sharma Abstract: Humans have always been lured by the idea that they can use data to understand a phenomenon and make predictions about it. Learning analytics, in this sense, promise to understand and optimize learning and the environments in which it occurs by collecting data from learners and learning contexts. In this regard, this study systematically examines research on learning analytics through bibliometric, data mining, and analytics approaches. This paper argues that research interest in learning analytics is increasing steadily; some countries, higher education institutions, and researchers have a specific research agenda that indicates their intention to specialize in that field. It is also noted that there is a need for more interdisciplinary studies on learning analytics and a further need to merge technological capabilities with pedagogy. Based on the findings of text-mining and social network analysis, the following themes were identified: (1) learning analytics to improve teaching and personalize learning,(2) hegemony of data-driven teaching and learning practices, (3) multimodal learning analyticsas the next generation practice, (4) learning design for learning analytics, (5) formative assessment through learning analytics, (6) learning analytics for social online learning spaces, and (7) privacy and ethical concerns to overcome. This paper suggests focusing on issues such as ethics and privacy and warns researchers to pay attention to the risks of both an educational panoptic society and quantified decision-making processes. Furthermore, rather than relying on algorithms, it is suggested to incorporate social values and center the learners in the learning analytics processes. Finally, this paper asks the following: “If we quantify the learning processes, how can we benefit from the carpe diem of educational processes and then seize the beauty of teaching and learning?” Keywords: learning analytics, learning design, educational data mining, adaptive and personalized learning, diagnostic and predictive analytics . Highlights What is already known about this topic: • Learning analytics is an emerging, evolving, and data-driven field. • Learning analytics promises understanding and optimizing learning and the environments in which it occurs. What this paper contributes: • This paper critically analyzes the state of the art in learning analytics research. • This paper warns us about blindly adopted, data-dominated learning analytics strategies. Implications for theory, practice and/or policy: • There is a need for generating policies and strategies for data privacy and ethics. • There is a need for more interdisciplinary studies, merging the use of technology and pedagogy in learning analytics research. • Social aspects of teaching and learning should not be neglected in the learning analytics equation. • The Carpe Diem moment of teaching and learning should not be ignored. Introduction: Data-Driven, Digitally-Shaped Educational Landscapes If you torture the data long enough, it will confess to anything.–Ronald H. Coase Digital transformation processes have been widely adopted in the higher education landscape (Bozkurt & Sharma, 2022), which further inspired higher education institutions to access and use the large volume of data that is available in online learning environments. Besides, as noted by Clow (2013) and Sclater et al. (2016), learning management systems are used as the default learning space by many higher education institutions, which allows higher education institutions to collect each single digital footprint; the amount of this type of data is increasing and is easy to access. Furthermore, there is a tendency to benchmark the performance of the learners through quantified metrics. Statistical and computational capacities, doubled with powerful di

FAQs

What is the main purpose of learning analytics discussed in the paper?

The paper presents learning analytics as a way to improve the quality of learning experiences by analyzing different learning scenarios and learner contexts to understand and optimize learning.

Which methods does the study use to examine learning analytics research?

The study examines research through bibliometric review, data mining, and analytics approaches, including text-mining and social network analysis to derive themes.

What concerns does the paper raise about learning analytics?

It warns about blindly adopted, data-dominated strategies and highlights risks related to privacy, ethics, educational panopticism, and quantified decision-making processes.

Oct 15, 2026

Uploaded by Aran