AI-Driven Learning Analytics for Personalized Feedback and Assessment in Higher Education

The rapid recent use of learning analytics (LA) in higher education, specifically during the COVID-19 pandemic, allows the monitoring of users' behavior while learning. Using LA may promote students' learning outcomes but also intrude into their privacy. This study aimed to explore students’ behaviour and perceptions towards privacy and data protection when using LA for pedagogical needs, examine the privacy trade-off of students' willingness to share personal information in exchange for pedagogical benefits and understand the predicting variables for this privacy trade-off. A model was developed containing five groups of influencing variables (demographic data, perceptions, feelings, behaviour and awareness) on the privacy trade-off. A total of 1,014 students completed an online questionnaire. The results found that students do care about their privacy but are not aware of privacy and data protection regulations. They are willing to trade off privacy for pedagogical benefits, and they trust their academic institutions, but they want transparency. Age, a sense of security in the academic institution, behaviour, data misuse concern and institution management of students' personal information are the significant predictors for a privacy trade-off. It is important to engage students in the process as they are the main beneficiaries of LA and build trust between them and the institution. Implications for practice or policy: Academic institution should actively provide information to raise privacy awareness. Academic institutions should engage students in the process of using LA to create a high degree of trust. Universities should endorse LA policy, fostering it for pedagogical purposes. Instructors may utilise LA to enrich student learning, respecting their willingness to share pedagogical data. Academic institutions should provide a high level of transparency in order to build students’ trust in their institutions.

Potential benefits of learning analytics (LA) for improving students’ performance, predicting students’ success, and enhancing teaching and learning practice have increasingly been recognized in higher education. However, the adoption of LA in higher education institutions (HEIs) to date remains sporadic and predominantly small in scale due to several socio-technical challenges. To better understand why HEIs struggle to scale LA adoption, it is needed to untangle adoption challenges and their related factors. This paper presents the findings of a study that sought to investigate the associations of adoption factors with challenges HEIs face in the adoption of LA and how these associations are compared among HEIs at different scopes of adoption. The study was based on a series of semi-structured interviews with senior managers in HEIs. The interview data were thematically analysed to identify the main challenges in LA adoption. The connections between challenges and other factors related to LA adoption were analysed using epistemic network analysis (ENA). From senior managers’ viewpoints, ethical issues of informed consent and resistance culture had the strongest links with challenges of learning analytic adoption in HEI; this was especially true for those institutions that had not adopted LA or who were in the initial phase of adoption (i.e., preparing for or partially implementing LA). By contrast, among HEIs that had fully adopted LA, the main challenges were found to be associated with centralized leadership, gaps in the analytic capabilities, external stakeholders, and evaluations of technology. Based on the results, we discuss implications for LA strategy that can be useful for institutions at various stages of LA adoption, from early stages of interest to the full adoption phase.

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.

Despite the growing popularity of learning analytics in higher education, its development status in Asia was barely studied. This paper reports a study on the development of learning analytics in higher education in Asia. Semi-structured interviews were conducted with eight senior managers or senior academics from various tertiary institutions in Asia. The participants were asked about their institutions’ position on learning analytics, the progress in its implementation, factors leading to effective implementation, and challenges encountered, if any. The results showed that in those institutions where learning analytics has been implemented, it aimed mainly at enhancing student retention, pedagogy and student learning experience. Its effective implementation relies on support from senior management, and taking students’ views into account in decision-making. The participants’ institutions encountered difficulties due to teachers’ and students’ concerns, such as the increased workload and data privacy, as well as technical issues in data collection, processing and analysis. In short, though starting late in Asia, learning analytics has been gradually gaining attention and is being implemented. The future directions of research and practices in learning analytics are also discussed.

Higher education institutions recently have been drawing on methods from learning analytics to make decisions about learners' academic progress, predictions about future performance and to recognise potential issues. As the use of learning analytics in higher education is a relatively new area of practice and research, the intent of this paper is to provide an overview of learning analytics including a summary of some exemplar tools. Finally we conclude the paper with a discussion on challenges and ethical issues.

Learning analytics (LA) is a rapidly growing area of research that focuses on using data from learning technologies to improve the quality of students' outcomes. This paper aims to provide a comprehensive understanding of the current state of research on learning analytics technologies and how they can positively impact student outcomes. To achieve this, a systematic review using a PRISMA methodology was conducted. Inclusion and exclusion criteria were applied to select relevant papers, resulting in a final set of 31 papers for analysis. The identified papers were then organised and analysed. The findings show that the use of learning analytics has many benefits for both learners and instructors. However, its adoption by higher educational institutions has been slow and limited due to a lack of resources, funding, and skills. Four systematic reviews on the topic have been conducted, but they do not reveal any significant changes in the status of research and practice over the years. The analysed papers highlight the use of learning analytics for predicting student learning behaviours and identifying at-risk students who may benefit from targeted interventions. These interventions have been shown to improve students' academic performance and retention rates. Furthermore, learning analytics has also been used for technology enhanced learning and to improve overall academic outcomes for students. Moving forward, it is crucial to focus on overcoming the barriers to adoption that hinder the widespread use of learning analytics in higher education. Additionally, exploring alternative options beyond the traditional dashboard-based approach could offer new insights and improve the overall effectiveness of learning analytics systems. This research has implications for universities, learning staff and students.

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.

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.

There is growing rhetoric about the potential of learning analytics in higher education. There is concern about what the growing hype around learning analytics will mean for the reality. Will learning analytics be a repeat of past mistakes where technology implementations fail to move beyond a transitory fad and provide meaningful and sustained contributions to learning and teaching? How can such a fate be avoided? This paper identifies three paths that learning analytics implementations might take, with particular consideration to their likely impact on learning and teaching. An ongoing learning analytics project – currently used by hundreds of teaching staff to support early interventions to improve student retention - at a regional Australian university is examined in relation to the three paths, and some implications, challenges and future directions are discussed.

The Covid-19 pandemic has further fueled an increase of e-learning in higher education. The widespread use of online learning generates vast amounts of academic data. This data can be collected and analyzed with the help of Learning Analytics to improve teaching and learning. Although students are essential stakeholders of Learning Analytics, their views are underrepresented in current research. Therefore, this paper aims to give an overview of opportunities and threats regarding the use of Learning Analytics from students’ perspective. For this purpose, a qualitative study with 136 students was conducted, and the answers were coded and classified by multiple researchers. The results show a generally positive attitude toward Learning Analytics. Noticeable in comparison with existing research were small-scaled answers of participants that focus primarily on the course level and students’ everyday lives. The identified opportunities and risks provide a good foundation for further research.KeywordsLearning AnalyticsHigher educationStudents’ perspective

Education 5.0 challenges and sustainable development goals in emerging economies: A mixed-method approach

The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education.

This article explores potential uses, misuses, beneficiaries, and tensions of learning analytics in higher education. While those promoting and using learning analytics generally agree that ethical practice is imperative, and student privacy and rights are important, navigating the complex maze of ethical dilemmas can be challenging, particularly in situations involving competing principles. We tease out the ‘clouds’ behind the silver lining of learning analytics and propose a principles-based operational framework for ethical data governance to help educational developers navigate situations involving individual and institutional uses of learner data, where what might be legally and technologically permissible is not necessarily ethical.

Learning Analytics in Higher Education: Perspectives, Practices and Challenges

The emerging fields of academic analytics also the educational data mining are rapidly producing new hypothesis for gathering, analyzing, and presenting student data. Different Area of Computer applications and also business administrations have addition significant standing value in higher education. Thus the kind of education, students turn in these areas depends on the geo-economical and the social demography. Thus the decision making of an institution in these area of higher education dependent on several factors like economic condition of students, geographical area of the institution, quality of educational organizations etc. To have a strategic move for the development of importing knowledge in this area requires understanding the behavior aspect of these parameters. Thus the scientific apprehension of these can be had from obtaining patterns or recognizing the attribute behavior from early academic years. Farther, applying data mining tool to the preceding data on the attributes known will throw better light on the behavioral view of identified patterns.