A Descriptive Learning Analytics: An Online Learning Programmes and Load-shedding Conundrum

Retrieval practice or learning by testing using multiple choice or True/False Quizzes called Quizlets was utilized to study its impact on student performance in fully online Anatomy and Physiology I lecture and lab courses as well as in an upper division Histology course all offered in asynchronous fashion. Learning analytics data was generated when studying for exams in the form of the number of times students used the retrieval practice tool and what scores were earned from using the Quizlets. R‐squared analysis was also employed to produce goodness‐of‐fit graphs to determine if results met expected outcomes.Course design features: Blackboard ULTRA course management system no video lectures ebook chapters and web readings assigned use Quizlets as learning tool (100 – 150 questions per chapter) used Respondus Lockdown Browser to prevent cheating Anatomy and Physiology I lecture course results (150 students)Figure 1 shows the grade distribution for all lecture exams. Data displayed in Figure 2 show that grades earned for Quizlets match closely the increase in percentage seen in Figures 1. Figure 3 shows a strong correlation between the number of Quizlets done and the grade received in the exams. Figure 4 shows similar student numbers for combined A + B and D + F categories. And lastly, Figure 5 shows an R‐squared analysis which demonstrates a goodness‐of‐fit statistical measure of how close the data are to the fitted line. In this case, an R‐squared value of 0.4082 falls within the acceptable range of goodness‐of‐fit values in a field such as learning analytics as they apply to data collected from studies involving human subjects.Anatomy and Physiology I lab course results (150 students)Figures 6 compares grades obtained on lab exams with grades accumulated from doing the Quizlets as a study tool. It is interesting to observe that when compared to the Anatomy and Physiology lecture material in Figure 4, Figure 6 shows a lowering of the percentage of A grades for Quizlets (orange bar) and increase in the percentage of F grades for Quizlets while the exam grades for A (blue) and F stay the same. The value for Quizlets in the A category add up to 556.88 points and when divided by the student number results in an average score of 9.77 out of a possible 10 points for each Quizlet taken. For the F category, the Quizlets add up to a total of 107.94 and when divided by the 38 students in the F category give rise to an average value of 2.84 out of a possible 10 points per Quizlet. This indicates that students studying for the lab exam did not use the Quizlets as a study tool as much as the students did in the A category and explains well the increase in percentage of students in the F category on the chart in Figure 6. Figure 7 shows an R‐squared analysis for all lab exams and is on the lower end of acceptability for data generated in a human response mode compared to well controlled physical or chemical action processes.Histology course (250 students)Figure 8 shows a close approximation of the grade distributions for the combined lecture and lab exams when compared to the Quizlets taken. The R‐squared analysis in Figure 9 shows a value of 0.3137 and is acceptable for a goodness‐of‐fit approximation since the data was generated in a human response mode and not using a controlled physical or chemical action.

The purpose of the article is to analyse the needs of general education schoolteachers’ data literacy skills that are important for the effective use of learning analytics in the teaching-learning process. The theoretical part of the article presents the idea of big data in education, highlights the aspects of pedagogical value of learning analytics technologies, provides the overview of learning analytic tools. Some overview and comparison of spread of learning analytics tools in general education schools in Lithuania and Germany is presented in the context of data-driven education. The empirical part of the article presents some results from a big qualitative study of teachers’ experiences applying learning analytics tools in teaching - learning process. The main question of the current research is what data literacy skills teachers need in order to use learning analytics tools and make data based pedagogical decisions. Semi-structured interviews were conducted with 10 Lithuanian and 9 German teachers from general education schools, who already have had experience in working with learning experience platforms (digital learning platforms that integrate learning analytics tools). Interview data were analysed by means of content analysis. The results of the qualitative study showed that in order to use learning analytics tools it is important for teachers to have such skills as: digital literacy, data collection, data analysis and interpretation, etc. Comparative analysis of informants’ answers showed that teachers in Lithuania and Germany expressed similar needs for data literacy skills.

With the increase in online classes and our access to large amounts of learning and educational data, Learning Analytics (LA) has received much attention. LA is essential for student-centered education and can make a significant contribution to transforming the educational model. This paper describes the procedures and significance of LA and introduces classes on mechanical engineering using LA tools. In our class, we used the LEAF system consisting of BookRoll and LAView as the LA tool. By using the BookRoll to deliver explanations of pre-class assignments for a flipped classroom, we could analyze the learning activities in the LAView and monitor student understanding. Furthermore, we found a statistically significant difference in the time spent reading the material between the highest- and lowest-performing students.

Several studies have shown that complex nonlinear learning analytics (LA) techniques outperform the traditional ones. However, the actual integration of these techniques in automatic LA systems remains rare because they are generally presumed to be opaque. At the same time, the current reviews on LA in higher education point out that LA should be more grounded to the learning science with actual linkage to teachers and pedagogical planning. In this study, we aim to address these two challenges. First, we discuss different techniques that open up the decision-making process of complex techniques and how they can be integrated in LA tools. More precisely, we present various global and local explainable techniques with an example of an automatic LA process that provides information about different resources that can support student agency in higher education institutes. Second, we exemplify these techniques and the LA process through recently collected student agency data in four courses of the same content taught by four different teachers. Altogether, we demonstrate how this process—which we call explainable student agency analytics—can contribute to teachers’ pedagogical planning through the LA cycle.

In the context of expanding digital education and persistent global disparities in access, this study explores how learning analytics (LA) can enhance teaching effectiveness and student success in open and distance learning (ODeL) environments. Focusing on Mineral Exploitation IA, a first-year engineering module offered in a South African university's Diploma in Mining Engineering program, the study exemplifies the use of data-driven methodologies to address systemic educational challenges such as low pass rates, high dropout rates, and poor learner retention. This case study employs an interdisciplinary and mixed-methods research design, integrating educational data mining (EDM), behavioral analytics, and comparative analysis to assess student engagement, performance, and demographic context. Drawing on data extracted from the institution's Moodle learning management system, the study examines how students interact with online materials (e.g., video content, discussion forums), complete assessments, and vary in performance across geographic and socioeconomic boundaries. Findings reveal that students from remote or under-resourced regions—primarily in developing countries—face significant challenges in accessing digital platforms, often due to infrastructural and technological limitations. These constraints negatively impact their participation and performance, highlighting the interdependence of technological, pedagogical, and socio-economic systems in ODeL contexts. Methodologically, the study aligns with an applied research paradigm, while demonstrating adaptive methodological flexibility. It incorporates a comparative framework that crosses disciplinary boundaries—drawing from education, data science, development studies, and digital communication. In doing so, it situates learning analytics not merely as a technical tool, but as a transdisciplinary research instrument capable of responding to context-specific educational realities. The study recommends targeted pedagogical interventions, including the integration of low-bandwidth, high-accessibility tools such as WhatsApp-based academic support and e-tutoring. These interventions reflect a culturally and technologically responsive design logic, emphasizing methodological pragmatism rooted in lived student experiences. By connecting data-informed research, methodological innovation, and context-sensitive teaching practices, this study contributes to the growing field of transdisciplinary education research. It argues for a shift from content-centred instructional design toward learner-responsive, equity-oriented strategies. It demonstrates how the thoughtful use of learning analytics can foster inclusive and effective online learning environments.

AI-driven learning analytics applications and tools in computer-supported collaborative learning: A systematic review

Regardless of all the frameworks and models that have been proposed for starting Learning Analytics (LA) initiatives, LA adoption remains immature. LA research is still a growing field in some regions, and researchers and practitioners continue to experience institutional challenges for scaling LA adoption. Considering the need for using LA to improve student learning and program quality, it is important to bring stakeholders together to exchange ideas about how LA tools could be used in everyday practices. To meet this objective, this chapter presents the LALA Canvas: a conceptual model to support a participatory approach to LA adoption in higher education (HE). The LALA Canvas was developed within the LALA Project, and it proposes a template to guide group discussions at universities where LA adoption is still at an early stage. The LALA Canvas model has been used by the four Latin American universities affiliated with the LALA project and socialized with 371 HE stakeholders from universities from different Latin American countries, including Chile, Brazil, Costa Rica, and Perú. This chapter describes the LALA Canvas and the lessons learned from having used it to start conversations about the potential benefits of LA in different university settings.

Transparency and trustworthiness are among the key requirements for the ethical use of learning analytics (LA) and artificial intelligence (AI) in the context of social inclusion and equity. However, research on these issues pertaining to users is lacking, leaving it unclear as to how transparent and trustworthy current LA tools are for their users and how perceptions of these variables relate to user behaviour. In this study, we investigate user experiences of an LA tool in the context of career guidance, which plays a crucial role in supporting nonlinear career pathways for individuals. We review the ethical challenges of big data, AI, and LA in connection to career guidance and analyze the user experiences (N = 106) of the LA career guidance tool, which recommends study programs and institutions to users. Results indicate that the LA career guidance tool was evaluated as trustworthy but not transparent. Accuracy was found to be a stronger predictor for the intention to follow on the recommendations of the LA guidance tool than was understanding the origins of the recommendation. The user’s age emerged as an important factor in their assessment of transparency. We discuss the implications of these findings and suggest emphasizing accuracy in the development of LA tools for career guidance.

During the last few years, learning analytics (LA) has gained the interest of researchers in the field of education. Generally, LA is related with the development of methods that use educational data sets to support the learning process. Therefore, there is a need understanding how learners, educators, and institutions can best support this process. Thus, a framework is presented that tries to extend the collaborative three-dimensional virtual environments for educational by integrating a LA tool. The aim is to help the teacher to monitor and evaluate the students' learning process in these types of environments. It is the intention include a (1) comprehensive analysis of the currently available LA tools for educational, (2) design of a user-centered framework based the requirements gathered from the analysis, and (3) thorough evaluation of framework to allow identify possible behavior patterns of students within the environment, related to your preferences for materials and expertise.

Teacher regulation of cognitive activities during student collaboration: Effects of learning analytics

Despite promising outcomes in higher education, the widespread adoption of learning analytics remains elusive in various educational settings, with primary and secondary schools displaying considerable reluctance to embrace these tools. This hesitancy poses a significant obstacle, particularly given the prevalence of educational technology and the abundance of data generated in these environments. In contrast to higher education institutions that readily integrate learning analytics tools into their educational governance, high schools often harbor skepticism regarding the tools’ impact and returns. To overcome these challenges, this work aims to harness learning analytics to address critical areas, such as school dropout rates, the need to foster student collaboration, improving argumentation and writing skills, and the need to enhance computational thinking across all age groups. The goal is to empower teachers and decision makers with learning analytics tools that will equip them to identify learners in vulnerable or exceptional situations, enabling educational authorities to take suitable actions that are aligned with students’ needs; this could potentially involve adapting learning processes and organizational structures to meet the needs of students. This work also seeks to evaluate the impact of such analytics tools on education within a multi-dimensional and scalable domain, ranging from individual learners to teachers and principals, and extending to broader governing bodies. The primary objective is articulated through the development of a user-friendly AI-based dashboard for learning. This prototype aims to provide robust support for teachers and principals who are dedicated to enhancing the education they provide within the intricate and multifaceted social domain of the school.

Learning analytics involve collecting, analyzing, and visualizing the digital 'footprints' that learners leave behind as they interact with digital learning environments. Learning analytics inform ongoing refinements to improve educational design and teaching practice. To date, research suggests that educators have not fully capitalized on learning analytics, despite the growing availability of data generated through digital learning platforms, programmatic assessment, and competency-based training. In this article, the authors adapted an established "learning analytics lifecycle" framework to evaluate a representative example of digital learning in HPE: eduCAST, an online, video-based website for orthopaedic surgery training. The framework provided a stepwise approach for exploring the potential of learning analytics in eduCAST, identifying implementation gaps, and developing a transferable list of recommendations for educators in HPE. Demographic and engagement data of 141 registered eduCAST users were analyzed after being collected via website analytics. The findings revealed limitations and opportunities related to: (1) planning for the learning environment and its users, (2) the scope and specificity of available learning analytics data, (3) the purposeful use of data analysis techniques, and (4) the relative absence of educators' data-informed actions. Generic website analytics showed significant limitations: high website traffic did not correspond to meaningful learner engagement, nor did it provide useful data to inform ongoing website refinement. Indeed, these analytics produced more data mysteries than meaningful data stories. Consistent with prior reviews in the HPE literature, this worked example demonstrated that educators appear to make diminishing investments of effort and resources across the learning analytics lifecycle. The authors argue that HPE educators can benefit from using learning analytics frameworks to guide the design, implementation, evaluation, and long-term sustainability of digital learning environments. When thoughtfully collected, analyzed, and interpreted, learning analytics can enhance learners' experiences and outcomes, support educators' professional development, and inform continuous program refinement.

Wearable sensor technology is increasingly being integrated into educational settings, offering innovative approaches to enhance teaching and learning experiences. These devices track various physiological and environmental variables, providing valuable insights into student engagement, comprehension, and educational environments. However, the extensive and continuous data streams generated by these sensors create significant challenges for learning analytics. This paper presents a comprehensive review of research on learning analytics incorporating wearable technology, systematically identifying methods and approaches that address wearable sensor data challenges. We begin with a systematic review of wearable sensor technologies' historical development and the current state of sensor data in learning analytics. We then examine multimodal sensor applications in learning analytics and propose research and application trends aligned with educational development needs. Our analysis identifies three key challenges: ethical considerations, explainable learning analytics, and technological and data management issues. The paper concludes by outlining seven future development directions for wearable sensors in educational contexts.

Learning Analytics is a new field of research that appears as a link between educator data and students. The Learning Analytics is also able to provide information about decision making to understand and optimise the learning process. Early childhood education is believed to be very important because the learner is open and always tries new things and is considered very meaningful for future processes in the development of all aspects of their personality. In this study, we aimed at investigating the application of learning analytics and how the learning process on child development in early childhood education. The Article Search Process is carried out on two databases, ScienceDirect and IEEE. In this study, the most important keywords are Learning Analytics and early childhood. The results of the search are 45 articles: (31/45) ScienceDirect and (14/45) IEEE, from 2012 to 2017. They are thoroughly explored in the Learning Analytics process, data collection and pre-processing, analysis and action, and post- processing. The process of data collection is done by implementing Online Systems or Game-Based Learning: 58% e-Learning systems, 27% Learning Analytics systems, and 15% Game-based learning. Many research was conducted on samples from Post graduate, High school and Elementary school. The results showed that early childhood education had the advantage of the use of the new technology and in enchancing the child’s knowledge and skills. Such as creativity and logical intelligence in the introduction of shapes and numbers. In the further study, the concept of Learning Analytics in the form of assessment and feedback that is given to support the improvement of the objectivity in the learning process by collaborating with educational games which can be another beneficial to the early childhood education. Objective assessment and feedback may be the monitor and prediction which will also be analised for the efficiency and effectiveness in the learning proces through the use ofthe technology.

With the arrival of 'big data; in education, the potential was recognised for learning analytics to track students' learning, to reveal patterns in their learning, or to identify at-risk students, in addition to guiding reform and supporting educators in improving teaching and learning processes [1]. Learning Analytics dashboards have been used at all levels, including institutional, regional and national level [2]. In classroom use, while learning visualisations are often based on counts of activity data or interaction patterns, there is increasing recognition that learning analytics relate to learning, and should therefore provide pedagogically useful information [3]. While increasing numbers of technology-enhanced learning applications are embracing the potential of learning analytics at the classroom level, often these are aimed at teachers. However, learners can also benefit from learning analytics data (e.g. [4][5]).