Learning Analytics In Higher Education Research Articles

The Evolving Classroom: How Learning Analytics Is Shaping the Future of Education and Feedback Mechanisms

In the dynamic world of higher education, technological advancements are continually reshaping teaching and learning approaches, with learning analytics (LA) playing a crucial role in this transformation. This systematic literature review (SLR) explores the significant impact of LA in higher education, specifically its transformative role in personalizing and enhancing educational feedback mechanisms. Utilizing a wide range of educational data, LA facilitates a shift from generic to individualized feedback, leading to improved learning outcomes and equity. However, incorporating LA into higher education is not without challenges, ranging from data privacy concerns to the possibility of algorithmic errors. Addressing these challenges is vital for unlocking the full potential of LA. This paper also examines emerging LA trends, such as augmented reality, emotion-sensing technology, and predictive analytics, which promise to further personalize learning experiences in higher education settings. By anchoring these advancements within core educational principles, we foresee a future of education marked by innovation and diversity. This SLR provides an overview of LA’s evolution in higher education, highlighting its transformative power, acknowledging its challenges, and anticipating its future role in shaping a dynamic, responsive educational environment.

Assessment of Teachers’ Needs to Integrate Learning Analytics in Higher Education: The Morocco Case

Learning Analytics (LA) is a better solution for harnessing the enormous data that learners leave behind on learning platforms. To take full advantage of it, it needs to be well integrated by teachers. The aim of this study was therefore to explore the needs and willingness of teachers to integrate learning analytics into their courses. This study used a quantitative method to collect data based on a questionnaire distributed to 140 higher education teachers affiliated with Ibn Tofail University. The results of this study show that most teachers are willing and available to commit to the possibility of adopting learning analytics if training and preparation programs on the subject are implemented. They also have other needs for guidance, assistance, easy-to-use tools, and need access to all student data on the appropriate platforms. The study concludes by discussing and proposing a series of recommendations for successfully integrating learning analytics into higher education.

Learning Analytics in Higher Education: Perspectives, Practices and Challenges

Learning Analytics in Higher Education: Perspectives, Practices and Challenges

BELIEFS, VALUES AND EMOTIONS IN PRACTITIONERS’ ENGAGEMENTS WITH LEARNING ANALYTICS IN HIGHER EDUCATION

Internet entrepreneurs, EdTech companies, AI enthusiasts, and other powerful stakeholders around the world have promoted the idea that big data and learning analytics (LA) have the potential to revolutionise education. LA, defined as the continuous measurement, collection, analysis and reporting of data about learners and their context (Gašević et al., 2015, p. 1), is increasingly being used to track and evaluate what students do in internet-mediated environments. A growing body of literature has questioned the benefits attributed to the use of AI-based solutions and raised a number of concerns about the current developments in the education sector. Despite this growing interest among researchers, we know little about how the beliefs, values and feelings of different groups of educational practitioners shape how they engage with AI-driven learning analytics technologies and influence the evolution of the cultures of practice shaping the adoption of learning analytics. In this paper, we report on research that asks: how do culturally situated beliefs, values and emotions shape practitioners’ engagements with narrow AI in different contexts of practice? The research project as a whole examines these cultures of practice across three contrasting contexts. Here we will discuss early findings from one of these contexts – learning analytics in higher education. With insights from this research, we aim to contribute to empower practitioners in higher education and relevant stakeholders to foster the development of critical and reflective data cultures that are able to exploit the possibilities of learning analytics while being critically responsive to their societal implications and limitations.

Academic engagement and management of personalised active learning in higher education digital ecosystems

AbstractThe flexible, changing, and uncertain nature of present-day society requires its citizens have new personal, professional, and social competences which exceed the traditional knowledge-based, academic skills imparted in higher education. This study aims to identify those factors associated with active methodologies that predict university students’ learning achievements in a digital ecosystem and thus, optimize the learning-teaching process. The teaching management tool Learning Analytics in Higher Education (LAHE) has been applied to a 200-student non-probabilistic incidental sample spread over 5 different university courses, enabling a personalized learning-teaching process tailored to the needs of each group and /or student. Based on a pre-experimental design without a control group, an analysis through decision trees based on educational data mining has been undertaken on the predictive potential of the active methodologies employed, and their effects on students’ learning achievements. The criterion variable of the study was the final exam grade, and the explanatory variables included student characteristics, indicators of the teaching–learning process and non-cognitive factors. Results show that factors associated with active methodologies correctly predict a significant portion of the learning achieved by students. More specifically, the factors that have the greatest impact on learning are those related to academic engagement and to a student continuous learning process.

Mapping the connection between Learning Analytics and Learning Design

Over the past decade many have attempted to articulate the connection between Learning Design (LD) and Learning Analytics (LA) in the form of a framework or model. However, there are now so many of these that it is difficult for practitioners to determine which ones are best for which circumstances. In this workshop, participants will be introduced to a new LD/LA map which brings together the key elements from across the multitude of frameworks in order to assist in the operationalisation of learning analytics in higher education. The aim of the workshop is to apply the framework to learning scenarios to evaluate and critique its effectiveness in informing the development of LA systems and interventions. The outcome of the workshop will be a better understanding of the utility of the map and a shared vocabulary relating to how we can talk about the connection of LD and LA in educational environments.

Assessing the Readiness of Students in Adopting Learning Analytics in Higher Education

Purpose: Getting students involved in developing learning analytics (LA) services is a major challenge in the academic world. Despite calls for more stakeholder involvement, research into students' views and expectations of learning analytics services is scant. Improving end-user buy-in and resource planning is possible by addressing these concerns and learning about student expectations before rolling out LA.Methodology: An extensive literature review is included, with a focus on previous research into the value, existing models, and difficulties of implementing LA. To fill this void, we used the SHIELA approach to survey students' beliefs and expectations concerning LA adoption. Factor loading, construct validity, and discriminant validity tests are performed in SmartPLS 4 to ensure the accuracy and credibility of the data. A structural equation model further verified the relationship between students' anticipations and their actual acceptance of LA.Findings: Our research confirms that the effectiveness of LA adoption is highly influenced by the three highlighted constructs: privacy and ethics and organizational and meaningful expectations. The research also showed that the student considered creating a detailed learning profile for each module to be the best application of LA. They believed LA would give them feedback on their learning and help them make better decisions. They think institutions have a moral and legal responsibility to act, which means they should also include, promote, and empower students. Institutions must take the necessary precautions to reduce risks, even though teaching staff should share the load.Originality and Value: The research supplies university officials with data for improving their LA adoption tactics while also informing students of new ways in which LA might inform their own educational choices. This paradigm shift and the higher education sector's sustained, productive interest in learning analytics will lead to better results for students, universities, and society. Based on the findings, the study also suggests several avenues and topics for future research.

Do teaching staff trust stakeholders and tools in learning analytics? A mixed methods study

Learning analytics (LA) has gained increasing attention for its potential to improve different educational aspects (e.g., students’ performance and teaching practice). The existing literature identified some factors that are associated with the adoption of LA in higher education, such as stakeholder engagement and transparency in data use. The broad literature on information systems also emphasizes the importance of trust as a critical predictor of technology adoption. However, the extent to which trust plays a role in the adoption of LA in higher education has not been examined in detail in previous research. To fill this literature gap, we conducted a mixed method (survey and interviews) study aimed to explore how much teaching staff trust LA stakeholders (e.g., higher education institutions or third-parties) and LA technology, as well as the trust factors that could hinder or enable adoption of LA. The findings show that the teaching staff had a high level of trust in the competence of higher education institutions and the usefulness of LA; however, the teaching staff had a low level of trust in third parties that are involved in LA (e.g., external technology vendors) in terms of handling privacy and ethics-related issues. They also had a low level of trust in data accuracy due to issues such as outdated data and lack of data governance. The findings have strategic implications for institutional leaders and third parties in the adoption of LA by providing recommendations to increase trust, such as, improving data accuracy, developing policies for data sharing and ownership, enhancing the consent-seeking process, and establishing data governance guidelines. Therefore, this study contributes to the literature on the adoption of LA in HEIs by integrating trust factors.

Learning assessment in the age of big data: Learning analytics in higher education

Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that education lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper concludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education.

Untangling connections between challenges in the adoption of learning analytics in higher education

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.

Clouds in the silver lining of learning analytics: ethical tensions for Educational Developers

ABSTRACT 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.

A Novel Multi-Dimensional Analysis Approach to Teaching and Learning Analytics in Higher Education

E-learning platforms are being used in higher education to liberate teaching and learning from the constraints of time, pace, and space. These platforms provide rich media content, collaborative assessment, and analytics tools and support learning through a variety of online possibilities. However, the focus of most of this research is on learning analytics inside e-learning platforms and does not cover other related institutional surveys and educator professional development activities. This paper outlines a novel conceptual approach for the creation of a teaching and learning data warehouse analytics system that utilizes a Multi-Dimensional Analysis approach to teaching and learning analytics across these e-learning platforms in combination with other institutional data sources, such as various institutional surveys, tracking of professional development activities, and analysis of the use of the learning management system. The novel teaching and learning data warehouse analytics system (TLDWAS) is being developed at Lingnan University, a leading liberal arts university in Hong Kong. The genesis of this project is analyzing large volumes of teaching and learning data and presenting a big picture for senior management to gain insight from massive amounts of student courses and teacher evaluation data, fill the research gaps mentioned above, and help educators identify problems and solutions more effectively. The TLDWAS provides a visual interface for identifying some indicators and storing, classifying, and analyzing teaching and learning data in various forms to generate statistics, analyze models, and identify meaningful patterns in the data in order to improve teaching and learning. The TLDWAS not only provides more evidence-based decision-making for senior management but is also capable of being applied to identify patterns and relations pertaining to students’ academic performance, usage of e-learning systems, and associated staff development activities.

Implementation of Learning Analytics in Primary and Secondary School: A Systematic Literature Review

Increased adoption of educational technologies, the emergence of digital classroom concepts, and the interest in big data innovations have led to a growing awareness of the potential implementation of learning analytics to support learning development in educational institutions. However, most studies have focused on the implementation of learning analytics in higher education. As a result, research evidence and studies on actual and ongoing implementation in pre-higher education are still scarce. Therefore, there is a need to understand better the implementation of learning analytics at primary and secondary from the current learning analytics literature. This systematic literature review (SLR) aimed to identify learning analytics research that focuses on implementing learning analytics at pre-higher education levels, including in pre-school, primary, or secondary school. This SLR was carried out based on the SALSA framework to determine the protocol, search, appraisal, synthesis, analysis, and reporting approaches. The findings of the SLR support the arguments that the implementation of learning analytics in school is still in its infancy, where the implementation has been observed mostly in developed countries. Most of the Implementation is aimed at descriptive analytics, focusing on the purpose of monitoring, analysis, and feedback. The literature review has shown a lack of research on the implementation of learning analytics at the primary and secondary education levels.

Students' privacy concerns in learning analytics: Model development

AbstractUnderstanding students' privacy concerns is an essential first step toward effective privacy‐enhancing practices in learning analytics (LA). In this study, we develop and validate a model to explore the students' privacy concerns (SPICE) regarding LA practice in higher education. The SPICE model considersprivacy concernsas a central construct between two antecedents—perceived privacy risk and perceived privacy control, and two outcomes—trusting beliefs and non‐self‐disclosure behaviours. To validate the model, data through an online survey were collected, and 132 students from three Swedish universities participated in the study. Partial least square results show that the model accounts for high variance in privacy concerns, trusting beliefs, and non‐self‐disclosure behaviours. They also illustrate that students' perceived privacy risk is a firm predictor of their privacy concerns. The students' privacy concerns and perceived privacy risk were found to affect their non‐self‐disclosure behaviours. Finally, the results show that the students' perceptions of privacy control and privacy risks determine their trusting beliefs. The study results contribute to understand the relationships between students' privacy concerns, trust and non‐self‐disclosure behaviours in the LA context. A set of relevant implications for LA systems' design and privacy‐enhancing practices' development in higher education is offered.Practitioner notesWhat is already known about this topicAddressing students' privacy is critical for large‐scale learning analytics (LA) implementation.Understanding students' privacy concerns is an essential first step to developing effective privacy‐enhancing practices in LA.Several conceptual, not empirically validated frameworks focus on ethics and privacy in LA.What this paper addsThe paper offers a validated model to explore the nature of students' privacy concerns in LA in higher education.It provides an enhanced theoretical understanding of the relationship between privacy concerns, trust and self‐disclosure behaviour in the LA context of higher education.It offers a set of relevant implications for LA researchers and practitioners.Implications for practice and/or policyStudents' perceptions of privacy risks and privacy control are antecedents of students' privacy concerns, trust in the higher education institution and the willingness to share personal information.Enhancing students' perceptions of privacy control and reducing perceptions of privacy risks are essential for LA adoption and success.Contextual factors that may influence students' privacy concerns should be considered.

Learning Analytics implementations in universities: Towards a model of success, using multiple case studies


 
 
 In these pioneering days of Learning Analytics in higher education, universities are pursuing a diverse range of in-house implementation strategies, with varying degrees of success. In this exploratory study we compare and contrast the approaches taken at three demographically different Australian universities. The comparison is made in the context of Delone and McLean’s information system success model (1992). In time, a consensus-driven method for using Learning Analytics to improve student learning outcomes will eventuate, including individualized learning, but we are still some distance from this level of maturity. It seems likely that user-friendly proprietary platforms will prosper in the climate of uncertainty. Participants in the study see potential in Learning Analytics but are not sure about how best to realize that potential as the implementation of Learning Analytics systems at Australian universities are still very much in their infancy. Proprietary approaches offering sophisticated functionality seem likely to emerge and take precedence over the trial and error approach. This study addresses an apparent gap in the research as limited studies exist targeting both learning analytics and information system success. The methodology taken explores the research topic through a qualitative lense utlising thematic analysis. The study concludes that digital interventions such as Learning Analytics has great potential to optimize teaching and learning practices. Information systems success research can provide insights into what works and what does not in terms of Learning Analytics implementations. The discipline needs to be systematized for efficient implementation, and must deliver tangible benefits over time.
 
 

A systematic analysis of learning analytics using multi-source data in the context of Spain

ABSTRACT Learning analytics (LA) employs educational data to improve the timeliness of support for learners. Apart from technical aspects, there is a need to understand social complexities brought about by different stakeholders, so as to systematise the adoption of LA in Higher Education (HE). We present an analysis of the situation, needs and challenges of LA in the context of Spanish HE, considering managers’, teachers’ and students’ perspectives. Qualitative research is employed using recursive abstraction. Results reveal that the level of institutional adoption is low and none of the analysed institutions had an LA policy. Furthermore, only two of these institutions had an initial LA strategy. While the institutions shared some commonalities in their objectives for LA, chosen tools and adoption challenges, the distinct differences in the political contexts and institutional practices among the institutions reaffirmed that LA solutions and services cannot be implemented in the same manner. Moreover, different needs for LA and concerns are identified about its adoption among managers, students and teachers. These observations lead to our conclusion that the main challenges to implement LA in Spain are not related to technological issues but to the social and cultural issues rooted in institutions and those associated with different stakeholders.

Exploring students’ expectations of learning analytics: A person-centered approach

In order to successfully implement learning analytics (LA), we need a better understanding of student expectations of such services. Yet, there is still a limited body of research about students’ expectations across countries. Student expectations of LA have been predominantly examined from a view that perceives students as a group of individuals representing homogenous views. This study examines students’ ideal (i.e., representing their wanted outcomes) and predicted expectations (i.e., unveiling what they realistically expect the LA service is most likely to be) of LA by employing a person-centered approach that allows exploring the heterogeneity that may be found in student expectations. We collected data from 132 students in the setting of Swedish higher education by means of an online survey. Descriptive statistics and Latent Class Analysis (LCA) were used for the analysis. Our findings show that students’ ideal expectations of LA were considerably higher compared to their predicted expectations. The results of the LCA exhibit that the Swedish students’ expectations of LA were heterogeneous, both regarding their privacy concerns and their expectations of LA services. The findings of this study can be seen as a baseline of students’ expectations or a cross-sectional average, and be used to inform student-centered implementation of LA in higher education.

The Educators’ Datafied Present and Future: Complexity as an Approach to Developing Educators' Data Literacies

In the higher education context, an increasing concern on the technical or instrumental approach permeates attention to academics’ data literacies and faculty development. The need for data literacy to deal specifically with the rise of learning analytics in higher education has been raised by some authors, though in spite of some focus on the need to develop academics’ data literacy to embrace fair practices, this literature is often also rooted in a technical or data-driven perspective. In this paper, the authors summarize an empirical study based on 137 articles using the terms “data literacy,” “teachers,” and “faculty development,” spanning from 2014 and 2019. The findings point out that out of the total, 78 papers reviewed took an instrumental, data science-focused perspective on data literacy, were the technical abilities like extracting data and interpreting or reporting appropriately (authors, in press). Data safety and effective data management perspectives accounted for another 35 of the 137 articles. Only seven took up data literacy from a critical perspective, while only five looked at the pedagogical practice. These preliminary findings require awareness and discussion on the light of appropriate faculty development approaches and activities. We introduce some recommendations aimed at understanding data as a complex emerging phenomenon in our societies, which requires building the literacies to face their negative effects like data surveillance and algorithmic biases, but also, to uncover its emancipatory power.

Mapping the Ethics of Learning Analytics in Higher Education: A Systematic Literature Review of Empirical Research

Ethics is a prominent topic in learning analytics that has been commented on from conceptual viewpoints. For a broad range of emerging technologies, systematic literature reviews have proven fruitful by pinpointing research directions, knowledge gaps, and future research work guidance. With these outcomes in mind, we conducted a systematic literature review of the research on ethical issues that have been empirically approached in the learning analytics literature. In our final analysis, 21 articles published in the period 2014–2019 met our inclusion criteria. By analyzing this data, we seek to contribute to the field of learning analytics by 1) characterizing the type of empirical research that has been conducted on ethics in learning analytics in the context of higher education, 2) identifying the main ethical areas addressed in the selected literature, and 3) pinpointing knowledge gaps.

Unresolved Privacy and Ethics Issues Related to Learning Analytics in Higher Education and Academic Librarianship

Learning analytics involve big data collection, analysis processes, and technology that are used in higher education institutes and academic libraries to support student success and perform organizational assessment. Since these processes require the input of personally identifiable student and patron information to be effective, there are major ethical and legal considerations that must be addressed concerning privacy. This article demonstrates that privacy concerns about learning analytics can be mitigated by requiring informed consent from participants, establishing protocols for the collection and management of personally identifiable information, and advocating privacy rights of patrons. By synthesizing and expanding on viewpoints from the literature, this article offers recommendations pertaining to the collection, analysis, and management of patron data that are gathered for the purpose of learning analytics.