Using learning analytics to alleviate course and student support administrative load for large classes: a case study

Learning analytics leverages the use of technology to gather and analyse data about student performance, engagement, and learning behaviours, which can help educators make informed decisions about how to improve learning outcomes. In higher education, learning analytics can provide insights into student engagement, performance, and learning pathways. Collaborative learning in higher education involves a group of students working together towards a common goal or task. Collaborative learning encourages students to work together to solve problems, analyse information, and make decisions, especially in self-directed learning environment. Through discussion and debate, students develop critical thinking skills and learn to approach problems from different perspectives to enhance problem-solving skills. Collaborative learning provides opportunities for students to work with others from diverse backgrounds, helping them develop interpersonal skills such as communication, teamwork, and leadership. While collaborative learning helps students to foster critical thinking and develop interpersonal skills, students’ activities and engagement in collaborative learning are not properly assessed and measured. Student performance indicators are highly dependent on the learning activities and resources used in the learning management system based on individual basis. The ability and potential of learning analytics to track students’ behaviour and performance in team, and to monitor the effectiveness of their sharing and communication is not fully utilised in higher education. This paper addresses this issue and aims to provide an overview of collaborative learning analytics. The overview elaborates essential elements in collaborative learning and defines features in analytics to support collaborative learning. The overview is expected to guide to educators and developers in promoting and assessing students’ performance based on collaborative works.

Although blended learning has many opportunities for flexible learning, it also includes challenges.\nOne of the major challenges is to keep students motivated. An opportunity often overlooked by\neducational scientists is the vast amount of data generated by learning management systems, or\nsummarised in one word: Learning Analytics (LA). LA could be used to promote students’ motivation\nby providing teachers insight into students’ learning activities within a blended learning context.\nHowever, this research field is still underdeveloped. Little is known about students’ perceptions of LA,\nbut this information is fundamental to design supportive LA interventions. Therefore, in a first phase,\nthis study aims to investigate how motivated student teachers are in a blended learning context.\nFollowing self-determination theory, this study examines whether the student teachers’ basic needs\nare fulfilled. In a subsequent second phase, the student teachers’ perceptions of a hypothetical use of\nLA are explored. In December 2016, a study was conducted in a blended learning setting in teacher\neducation. The results indicate that student teachers’ basic needs are not fulfilled. The second major\nfinding suggests that student teachers have different opinions about the use of LA, and overall that the\nmajority of the student teachers is not convinced about the added value. Yet, these perceptions are\nbased on a hypothetical use of LA and not on student teachers’ own experiences. Future research\nshould investigate what student teachers’ perceptions are after a LA intervention is conducted in the\nblended learning setting, and whether this intervention can give appropriate input to the teacher\nenabling him or her to better accommodate students’ basic needs.

Game-based learning is a well-established educational concept that has been rejuvenated through digital technology. Game-based learning leverages the interactive nature of games to promote active knowledge acquisition in students. The effectiveness of game-based learning centers on two key components: competition and engagement. This theory is rooted in the belief that engagement occurs when students actively perform tasks within a stimulating game environment, fostering active learning. Modern digital games seamlessly integrate educational content with gameplay, facilitating the transfer of information from short-term to long-term memory. Game-based learning is applicable across various educational settings, including higher education in the health professions. In higher education, digital games stimulate active engagement, supporting the development of problem-solving skills and collaboration in academic environments. Furthermore, game-based learning instills a valuable lesson in students — the ability to learn from mistakes and failures while completing the assignment. Game-based learning provides a safe space for students to learn, offering scaffolding through simulations, preparing them for course performance. Some of the most popular educational games are those that allow educators to create questions with answers, enabling students to compete for points. Most of these games can be specifically tailored for the current content, are accessible online, and multiple groups can participate simultaneously. In this study, we developed several web-based games for multiple student cohorts, incorporating them into class activities and making them available as solo-play options within the Learning Management System (LMS) as SCORM packages. In specific courses such as biochemistry and physiology, students were invited to engage in free online games developed by the instructor, employing various free platforms. The games were optional, and participation was not tied to grading. These games featured questions designed at a Bloom's Taxonomy level comparable to the summative assessments in the course. The games fostered a competitive, enjoyable atmosphere that was well-received by students. These game questions served as additional checkpoints for revisiting key concepts and essential details from the class sessions. Importantly, as these questions were not associated with grading, it ensured a stress-free learning experience. As assessment resources, students were able to access the games through the LMS, reviewing questions and playing them repeatedly to learn from their errors and reinforce their understanding. This approach allowed students to engage in self-assessment while feeling that they were studying, even outside the classroom. To investigate whether student engagement with games correlates with improved academic performance, we tracked access to the games in the LMS during the semester. We recorded the number of times each student accessed the games and correlated this data with their course grade. In addition, we distributed an end-of-semester survey to gather students' perceptions regarding the utility of game-based learning in their learning process. The feedback from students was overwhelmingly positive, highlighting the significant benefits of game-based learning in their learning journey. However, our analysis revealed that there was no statistically significant correlation between student engagement with the games and their course grade. In conclusion, our study demonstrates that the integration of game-based learning activities into the classroom environment enhances student engagement, promotes active learning, and receives strong positive feedback from students. While game-based learning offers a valuable tool for reinforcing key concepts and providing a stress-free environment for self-assessment, our findings do not suggest a direct correlation between student engagement with game-based learning and improved course grades. This intriguing result underscores the complex relationship between student engagement and academic performance, suggesting that other factors play a pivotal role in determining student outcomes in higher education. None. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Business Intelligence (BI) represents a pivotal advancement in leveraging information technology to enhance organizational performance. BI tools serve as crucial aids in decision-making processes by furnishing requisite insights. In higher education institutions, BI can contribute to leaders and managers in providing perspectives related to academics, learning, and management. Central to BI development is the meticulous gathering of requirements, a process pivotal in identifying organizational informational and knowledge needs. This involves employing various methods such as interviews, observation, and analysis, including leveraging learning analytics to discern data utility for enhanced learning processes. Various studies show that learning analytics contributes to improving the learning and education process. On the other hand, learning analytics requires activity data that is integrated, subject oriented, and time series which are aligned with the characteristics of the data warehouse (DWH) as the main component of BI. This research endeavors to develop BI utilizing academic and e-learning data, exemplified through a case study of Telkom University's Academic Systems and Learning Management Systems (LMS). This study aims to provide actionable insights into the intersection of BI and learning analytics, ultimately enhancing educational processes and organizational decision-making capabilities. By integrating learning analytics into BI development, the resultant BI systems can cater not only to current managerial demands but also anticipate future analytical needs. The implementation of the multidimensional schema was successfully executed. This process involved mapping data from the academic information system and the LMS as data sources to the data warehouse, the Extract, Transform, and Load (ETL) process, and development of the prototype. The testing on the prototype indicated that the prototype meets the intended requirements and provides valuable insights through its comprehensive reporting capabilities. This demonstrates the effectiveness of the implemented multidimensional schema, ETL process, and the overall design of the reporting dashboard.

Learning analytics is an emerging technique of analysing student participation and engagement. The recent COVID-19 pandemic has significantly increased the role of learning management systems (LMSs). LMSs previously only complemented face-to-face teaching, something which has not been possible between 2019 to 2020. To date, the existing body of literature on LMSs has not analysed learning in the context of the pandemic, where an LMS serves as the only interface between students and instructors. Consequently, productive results will remain elusive if the key factors that contribute towards engaging students in learning are not first identified. Therefore, this study aimed to perform an extensive literature review with which to design and develop a student engagement model for holistic involvement in an LMS. The required data was collected from an LMS that is currently utilised by a local Malaysian university. The model was validated by a panel of experts as well as discussions with students. It is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

The development and implementation of learning analytics as a mechanism to support student success has been an emerging trend within Higher Education. Previous research identifies that learning analytics is an innovative educational development but recognises that little attention has been paid to evaluating its effectiveness or pedagogic usefulness. Researchers recognise that learning analytics is a new field in need of further research to aid its credibility within the educational arena. This research study provides a better understanding of learning analytics to support student success through the examination of opportunities and challenges of learning analytics from a multi-stakeholder perspective. This study also demonstrates how learning analytics can be successfully implemented within Higher Education. Through an interpretivist paradigm, this cross-sectional research study captures the unique experiences of students, academic staff and learning analytics experts. Data collected through twelve semi-structured interviews and three student focus groups enabled the researcher to gather a broad understanding of learning analytics from those involved and provides an holistic portrayal from this cultural group. The main findings of this study suggest that learning analytics need to have a clear context and purpose within Higher Education to ensure successful development, effectiveness and pedagogic usefulness. Effective organisational change, culture, academic and student engagement, ownership and motivation are paramount. Findings also indicate disparities in the implementation of learning analytics within Higher Education, which require resolution to ensure success, and there is some discussion about how challenges can be overcome to ensure effective institutional adoption and student success.

This article explores the complex challenges of co-designing an AI- and learning analytics (LA)-integrated learning management system (LMS). While co-design has been proposed as a human-centred design approach for scaling AI and LA adoption, our understanding of how these design processes play out in real-life settings remains limited. This study is based on ethnographic fieldwork in primary and secondary schools and employs a relational materialist approach to trace, visualise, and analyse the increasingly complex and transformative relations between a growing number of actors. The findings shed light on the intricate ecosystem in which AI and LA are being introduced and on the marketisation of K-12 education. Instead of following a rational and sequential approach that can be easily executed, the co-design process emerged as a series of events, shifting from solely generating ideas with teachers to integrating and commercialising the LMS into a school market with an already high prevalence of educational technology (EdTech). AI and LA in education, co-design and data-driven schooling served as negotiating ideas, boundary objects, which maintained connectivity between actors, despite limited AI and LA implementation and the development of a stand-alone app. Even though teachers and students were actively involved in the design decisions, the co-design process did not lead to extensive adoption of the LMS nor did it sufficiently address the ethical issues related to the unrestricted collection of student data.

Purpose The purpose of this paper is to present a systematic review of the mounting research work on learning analytics. Design/methodology/approach This study collects and summarizes information on the use of learning analytics. It identifies how learning analytics has been used in the higher education sector, and the expected benefits for higher education institutions. Empirical research and case studies on learning analytics were collected, and the details of the studies were categorized, including their objectives, approaches, and major outcomes. Findings The results show the benefits of learning analytics, which help institutions to utilize available data effectively in decision making. Learning analytics can facilitate evaluation of the effectiveness of pedagogies and instructional designs for improvement, and help to monitor closely students’ learning and persistence, predict students’ performance, detect undesirable learning behaviours and emotional states, and identify students at risk, for taking prompt follow-up action and providing proper assistance to students. It can also provide students with insightful data about their learning characteristics and patterns, which can make their learning experiences more personal and engaging, and promote their reflection and improvement. Originality/value Despite being increasingly adopted in higher education, the existing literature on learning analytics has focussed mainly on conventional face-to-face institutions, and has yet to adequately address the context of open and distance education. The findings of this study enable educational organizations and academics, especially those in open and distance institutions, to keep abreast of this emerging field and have a foundation for further exploration of this area.

The barriers to accessing electronic learning materials have significantly decreased in the rapidly evolving field of education technology. Learning Analytics (LA) has emerged as a valuable tool for improving institutional decision-making and student outcomes. This paper addresses the lack of detailed research focusing on the foundational stages of LA in Canadian universities by developing an automatic LA system to identify engineering students needing support. The initial phase involves examining LA practices in other institutions and evaluating the current landscape of LA implementation at scale among Canadian universities. Additionally, the paper explores the limitations of the LA features in Brightspace Learning Management System (LMS), specifically the Student Success System (S3), for detecting at-risk students and providing interventions. While statistical results demonstrating the effectiveness of LA in Canadian universities are lacking, proactive strides have been made, and more than 50% of universities investigated have implemented or planned to implement LA tools. This underscores the growing recognition of the need for LA to enhance academic performance and student success. The paper concludes by proposing a robust LA architecture as a reference model that prioritizes early identification of at-risk students which tackle the limitations of current LA features in Brightspace LMS.

Learning analytics (LA) has emerged as a powerful approach to improving student success, personalizing learning, and guiding institutional decision-making. Despite the availability of rich learner data from learning management systems (LMS) and digital platforms, the use of LA remains underdeveloped in higher education. Most institutions confine analytics to descriptive reporting - tracking logins, task completion, and grades, without advancing toward predictive, personalized, or intervention-oriented applications. This underutilization results in missed opportunities to identify at-risk learners early, support adaptive pathways, and continuously refine curriculum design. This concept paper adopts a qualitative and conceptual research design, drawing on recent literature and theoretical models such as the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI), to propose the Integrated Learning Analytics Adoption Framework (ILAAF). The framework emphasizes five strategic pillars - vision and governance, capacity building, technology integration, pedagogical alignment, and continuous evaluation, supported by an operational workflow that transforms raw data into actionable insights. The findings highlight that adopting ILAAF can shift LA from passive reporting to active, learner-centered practice. The paper recommends strategic institutional adoption, capacity development, and ethical governance to ensure LA becomes a dynamic driver of engagement, equity, and long-term student success.

The purpose of this study is to present data on the use of learning analytics in the field of education. For this purpose, data available in the Web of Science database were examined using bibliometric analysis. The publication distribution varied between 2011 and 2022, with the number of publications reaching its peak in 2020 and declining thereafter. Findings from co-authorship analysis indicate that Dragan Gašević is the most prolific author and the most cited researcher in the field of learning analytics. Results of co-authorship analysis by country suggest that researchers affiliated with institutions in Australia engage in more collaborative work and cooperation. Results of co-authorship analysis by organizations demonstrate that Monash University in Australia is the organization with the highest level of collaboration with other organizations. The most cited work is Ferguson's (2012) article titled "Learning analytics: drivers, developments and challenges." The most frequently used keywords in conjunction with learning analytics are higher education, educational data mining, online learning, learning design, and self-regulated learning. In recent years, there has been a focus on learning analytics related to learning management systems (LMS) rather than MOOCs in learning environments. The British Journal of Educational Technology, which was established in 1970, is the most cited journal that is commonly referenced in the articles. It is hoped that this study will serve as a valuable reference for researchers interested in the field and guide them in making accurate inferences based on a broad perspective.

Educational institutions are increasingly turning to learning analytics to identify and intervene with students at risk of underperformance or discontinuation. However, the extent to which the current evidence base supports this investment is currently unclear, and particularly so in relation to the effectiveness of interventions based on predictive models. The aim of the present paper was to conduct a systematic review and quality assessment of studies on the use of learning analytics in higher education, focusing specifically on intervention studies. Search terms identified 689 papers, but only 11 studies evaluated the effectiveness of interventions based on learning analytics. These studies highlighted the potential of such interventions, but the general quality of the research was moderate, and left several important questions unanswered. The key recommendation based on this review is that more research into the implementation and evaluation of scientifically driven learning analytics is needed to build a solid evidence base for the feasibility, effectiveness and generalizability of such interventions. This is particularly relevant when considering the increasing tendency of educational institutions around the world to implement learning analytics interventions with only little evidence of their effectiveness. Practitioner Notes What is already known about this topic? Drop‐out rates and underachivement is a significant issue at most Western universities. Learning analytics have been shown to predict student performance and risk of dropping out. Interventions based on learning analytics have emerged in recent years, some reportedly successful. What this paper adds The paper also reviews and synthesizes the evidence on the effectiveness of learning analytics interventions targeting student underperformance, experience and discontinuation. The paper compares and contrasts past and current learning analytics methods and foci, and makes recommendations for the future research and practice. It critically synthesizes the current evidence base on learning analytics interventions, which is a field that is in constant flux and development. Implications for practice and/or policy The paper focuses on an increasing part of higher education with the goal of validating learning analytics methods and usefulness. The paper makes evidence‐based recommendations for institutions wishing to implement learning analytics programs and/or interventions. The paper makes evidence‐based recommendations for instructors as well as researchers in the field.

The increasing use of Learning Management Systems (LMS) in higher education generates large amounts of student activity data that have the potential to provide deeper insights into learning processes. However, in practice, these data are still rarely analyzed systematically to understand variations in students’ learning activity patterns, limiting their practical use in supporting teaching and learning. This study aims to explore students’ learning activity patterns in an LMS using a clustering approach based on activity data.This research utilizes the publicly available Open University Learning Analytics Dataset (OULAD), focusing on a single course and a single academic term. LMS activity data were processed through data cleaning and feature extraction, followed by student clustering using the K-Means algorithm. The quality of the clustering results was evaluated using the Silhouette Score, and visual analysis was applied to support the interpretation of the results.The results indicate that students’ learning activities can be grouped into two main patterns, namely a group of students with high learning activity and a group with lower or moderate activity levels. These findings highlight the existence of heterogeneous learning behaviors among students, even within the same learning context.The identified learning activity patterns provide an initial foundation for utilizing LMS data to monitor student engagement and to support the development of more responsive, data-driven learning approaches in higher education.

As technology progresses, understanding online learner's behavior is the key for successful implementation of digital learning. The more we know about learners, the more tailored learning experiences will be. Analyzing the learner's preferences will help to predict the behavior of learner's so that required support and guidance can be provided on time. For achieving this much more efficient systems are required to process all types of the learner interactions with the learning management system during their each learning activity. This paper discusses the importance of learning analytics, and presents its usage in eLearning. It describes how learning analytics can help the learners, instructors and institutions to look into ongoing learning processes and how the online educational processes can be improved. It also depicts about the proposed system how the learning styles can be used for improving learning experience.

The current scenario of higher education reflects an increased permeation of digital technology, with this integration has influenced both learning and teaching practices. One of the main impacts has been in the advancement of Learning Management Systems (LMS). By facilitating synchronous and asynchronous communication and interactions linked to a virtual environment, LMSs have become integral to higher education. Learning Analytic Dashboards (LAD) are the digital platforms used by educational institutions for collecting, measuring, analysing and reporting data concerning learners and their activities and achievements. Related to that, as an area of research and development, learning Analytics is a rapidly growing field of LMS and of particular significance to LAD design. Many universities are utilising LADs, but guidelines to support effective design underpinned by research are limited. This study aims to assess the role of LAD design in optimising learning by influencing factors such as student motivation, engagement and achievement. The significance of this study is that it will offer novel insights on principles that should be adopted while designing LADs capable of leading students towards better learning experience and performance. Based on implications from previous studies as well as an empirical investigation on the need for such tools, this study will develop a prototype of a LAD that befits with student-facing demands.