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Learning analytics has emerged as a new domain for identifying students’ behaviors, academic performance, academic achievement, and other related learning issues. Given its paramount importance and recency, several review studies were conducted. However, the previous reviews have mainly focused on the behavioral, affective, cognitive, and metacogni...
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... shown in Fig. 2, the learning analytics consists of four primary levels, including descriptive, diagnostic, predictive, and perspective. The "descriptive" level describes what learners do and what happens in the teaching and learning environment. The "diagnostic" level refers to the stage that covers the factors that affect students' performance and ...
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Background
Instructor scaffolding is proved to be an effective means to improve collaborative learning quality, but empirical research indicates discrepancies about the effect of instructor scaffoldings on collaborative programming. Few studies have used multimodal learning analytics (MMLA) to comprehensively analyze the collaborative programming p...
Citations
... Referring to the latest research work [5], LA is classified into four main levels which are descriptive, diagnostic, predictive and perspective levels as shown in Figure 1. ...
he field of Learning Analytics (LA) has witnessed remarkable growth, with a growing emphasis on the utilization of data-driven insights to enhance educational practices. Learning Analytics, encompassing the acquisition, analysis, and interpretation of student data, holds immense promise in transforming education. This review paper synthesizes the key advancements in Learning Analytics, focusing on its definition, benefits, and various levels of learning analytics. A comprehensive literature review has been conducted to delve into existing platforms, LA levels, and technologies. It critically evaluates the significance of predictive Learning Analytics in identifying trends and patterns in educational data. Moreover, the review delves into the integration of Artificial Intelligence (AI) in LA, highlighting its multifaceted utility, from personalized recommendations to intelligent tutoring systems. Several case studies are examined to underscore the real-world applications of AI models in Learning Analytics. This paper offers insights into the advantages of AI-driven LA, such as early intervention and adaptive learning. Challenges and ethical considerations in AI-powered LA are also discussed. Furthermore, it shines a spotlight on the field of machine learning within Learning Analytics, emphasizing its role in automating data analysis and prediction, thus streamlining educational processes. This comprehensive review provides a foundational understanding of the evolving landscape of Learning Analytics, AI, and Machine Learning in education.
... Big data and learning analytics present opportunities and challenges for education, and literature [22] provides an overview of the use of big data analytics in education, examining the challenges faced in the field and proposing strategies to address them. Learning analytics has become a brand new field for identifying student behavior, academic performance, etc. Literature [23] reviewed research on analytics learning and found that the existing research results are not well developed and an overall lack of a review of learning analytics research from the perspective of the scope of learning analytics research. Data-driven requires teachers to have strong analytical skills, and literature [24] discusses the impact of Industry 4.0 on education, pointing out that colleges and universities must transform traditional teaching practices and change traditional teaching concepts in order to adapt to the development of the times. ...
In order to gain insights into the key factors affecting students’ success in online education, this paper extracts students’ online learning behavioral feature indicators through the behavioral record data in the online learning platform, applies the attribute approximation algorithm based on the Bayesian Fuzzy Rough Set (IDB-BRS) model to attribute approximation of the behavioral indicators, and utilizes the improved Apriori algorithm to mine the association rules between online learning behaviors and learning effects. The improved Apriori algorithm is used to establish association rules between online learning behaviors and learning effects. In comparison to the VPFRS model attribute approximation algorithm, the IDB-BRS model attribute approximation algorithm does not necessitate pre-given parameters and achieves superior classification accuracy and approximation time in the Soybean, Credit, and Balance datasets, thereby offering greater practical value. The association rules reveal that students who carefully study course resources, actively submit assignments, and study online frequently contribute positively to their success in online learning. This paper holds significant implications for enhancing the effectiveness of learning in online education.
... All areas of the world have witnessed an unprecedented acceleration in the use of innovative technological tools. Specifically within the educational sector, emerging technologies-including social and collaborative learning tools, intelligent and adaptive tutoring systems, as well as augmented and mixed reality applications-have been extensively integrated into conventional educational methodologies by examining the dynamics between digital innovations and learning processes (Hantoobi et al., 2021;Klašnja-Milićević et al., 2017).. In recent years, there has been an increased interest in researching the impact of SGs on learning outcomes in higher education, as it integrates gaming and learning functions (Girard et al., 2013). ...
Since the beginning of the 21st century, the number of studies on serious games has been surging, and more and more serious games are being used for educational purposes to facilitate students' learning and increase their motivation. This study systematically reviewed seven empirical studies on the application of serious games in higher arts education from 2019 to 2024, aiming to reveal the current state of recent research in the field, incorporating trends in the use of technology and the results of the impact on learners. The results show that most articles indicate that serious games are effective in higher arts education. Currently, researchers pay relatively little attention to this area, but this area has good research potential. In addition, integrating serious games with technologies such as augmented reality, virtual reality and mixed reality in higher arts education is a promising avenue for future research and development.
... Volume 23,Issue 02, 2024 Learning analytic and higher education Learning analytics can offer tremendous benefits in higher education by empowering faculty, administrators, and students with detailed information and data insights from student behavior. Learning analytics is the collection, measuring, and analysis of data to improve student success in higher education institutions (Hantoobi et al., 2021). ...
In our study we will be discussing a detailed analysis of the application of learning analytics in higher education from (2017-2023). As it Addresses the various methodologies that's being used in learning analytics while highlighting its strengths and weaknesses. By collecting data from a wide range of academic papers and reports the study focuses on the role of how learning analytics enhances to student's achievement, progress and overall, the quality of their education. It also provides a guideline for an effective execution of learning analytics, reaffirming the importance of data-driven, customized educational approaches. Eventually the study explores ethical implications and potential challenges related to learning analytics presenting an overview of their impact in the educational sector.
... Dashboards not only offer visual information for students to support self-regulation (studies 17, 18, and 20) but two studies showcase adaptive systems adjusting content based on student performance or needs (studies 6 and 16). These technologiesoften stand-alone or integrated into learning management systems like Moodle, Project-Voltaire, or Dydatepossess common technological affordances such as monitoring student progress, which is crucial for teachers to understand their students (Hantoobi et al., 2021;Valle et al., 2021). Monitoring also plays a role in prediction algorithms, utilizing Data Mining (DM) and Machine Learning (ML) techniques to interpret and predict student learning progress (Tomkins et al., 2016). ...
... Study 10 highlights the influence of VLA tools on teacher orchestration, awareness, assessment, and reflection in a technology-enhanced environment. While authors briefly touch upon using LA for examining and improving learning designs, Hantoobi et al.'s (2021) review underscores the formative stage of LA and decision-making in education, especially in lower education levels where LA research is neglected, and further development is needed. ...
Visual Learning Analytics (VLA) uses analytics to monitor and assess educational data by combining visual and automated analysis to provide educational explanations. Such tools could aid teachers in primary and secondary schools in making pedagogical decisions, however, the evidence of their effectiveness and benefits is still limited. With this scoping review, we provide a comprehensive overview of related research on proposed VLA methods, as well as identifying any gaps in the literature that could assist in describing new and helpful directions to the field. This review searched all relevant articles in five accessible databases-Scopus, Web of Science, ERIC, ACM, and IEEE Xplore-using 40 keywords. These studies were mapped, categorized, and summarized based on their objectives, the collected data, the intervention approaches employed, and the results obtained. The results determined what affordances the VLA tools allowed, what kind of visualizations were used to inform teachers and students, and, more importantly, positive evidence of educational interventions. We conclude that there are moderate-to-clear learning improvements within the limit of the studies' interventions to support the use of VLA tools. More systematic research is needed to determine whether any learning gains are translated into long-term improvements. Notes for Practice • VLA tools integrate visual and automated analysis to enhance educational decision-making in primary and secondary schools, yet evidence of its effectiveness remains limited. • This scoping review, based on Arksey and O'Malley's framework, explores VLA methods and their interventions in primary and secondary schools and highlights gaps in literature to guide future research and practice. • Results indicate moderate-to-clear learning improvements with VLA tools in classrooms, but call for more systematic research to assess long-term impacts.
... As learning analytics continues to mature, its role in enhancing the quality and equity of education is expected to expand, ultimately fostering a more inclusive, personalized, and effective learning ecosystem. Hantoobi et al. (2021) assert that "learning analytics has emerged as a new domain for identifying students' behaviors, academic performance, academic achievement, and other related learning issues" (p. 117). ...
In this chapter, the authors investigate the transformative impact of cognitive computing, learning analytics, and game-based pedagogy in enhancing teachers' professional development within the dynamic societal context. Employing a comprehensive review of scholarly literature, explore how these technological advancements shape the educational landscape, paying particular attention to their implications for teachers' professional development. The discussion illustrates that through adopting cognitive computing, educators can access personalized insights and resources tailored to their needs, fostering continuous growth. Learning analytics provide valuable data-driven feedback, enabling educators to make informed decisions to optimize teaching strategies. Furthermore, gamification engages teachers in immersive learning experiences, enhancing motivation and collaboration. This research highlights the potential of innovative technologies in shaping a more responsive and effective educational system.
... The number of studies using learning analytics in distance education is relatively high. Still, considering the increasing interest in these studies, a limited number of literature reviews have been published (Hantoobi et al., 2021). In this context, although it is seen that various systematic review studies on the use of learning analytics in educational environments have been conducted in the literature, only Kilis and Gülbahar (2016) have been found in the context of distance education. ...
This study aims to reveal the main trends and findings of the studies examining the use of learning analytics in distance education. For this purpose, journal articles indexed in the SSCI index in the Web of Science database were reviewed, and a total of 400 journal articles were analysed within the scope of this study. The systematic review method was used as the research methodology. The first study on the subject was published in 2011, and the publications continued to increase over the years. It was found that the publications on the subject were primarily found in “Computers and Education” and “Education and Information Technologies” journals. It was observed that China, USA, and Spain were the leading countries where the related studies were conducted. The studies primarily used the quantitative method, and university students were included as the sample. In addition, within the scope of learning analytics, it was observed that the data were mainly analysed with “regression analysis”, “correlation analysis”, “special algorithms-models”, “ANOVA”, and “cluster analysis” methods. It was determined that the most preferred platforms in the studies were learning management systems and MOOCs, learning behaviours were mostly examined, and log data were mainly used in this process. It was observed that the variables tested in the studies mainly consisted of students’ behaviours on the platform, learning performances, communication processes, dropout behaviours and course designs. Furthermore, in the studies examined, the advantages of learning analytics in the context of distance education are mostly related to the possibilities of improving the teaching process, and as disadvantages, it is stated that learning analytics is not suitable for use in some situations, negatively affect students’ performances, have limited interaction with students and are an expensive investment.
... The challenges of data privacy and ownership are, according to the reviews [2,15,16,18,22,[24][25][26][27][28]30,31], not sufficiently researched and discussed, as also pointed out in recent research literature [37,40]. However, those are issues that could ultimately end up being politicized, possibly discouraging further development of LADs, if not properly attended at the right level, i.e., between students or legal guardians, teachers, and the respective educational institutions [15,31]. ...
... However, those are issues that could ultimately end up being politicized, possibly discouraging further development of LADs, if not properly attended at the right level, i.e., between students or legal guardians, teachers, and the respective educational institutions [15,31]. Legal frameworks are proposed to mitigate concerns over LA [37,40], although this is a relatively new phenomenon with few examples in practice, which leaves room for interpretation of legal frameworks in different contexts [39]. A key ethical concern is data ownership. ...
... The reviews are in agreement about the notion that LA seems to provide efficient predictive models [11][12][13]15,18,21,22,25], as research has already indicated [37,40]. Prediction algorithms are highly specialized and can predict retention [11,13,18,20,21,25,31,34], study success, test score, drop-out, and students' well-being behavior [11,13,25]. ...
The promise of Learning Analytics Dashboards in education is to collect, analyze, and visualize data with the ultimate ambition of improving students' learning. Our overview of the latest systematic reviews on the topic shows a number of research trends: learning analytics research is growing rapidly; it brings to the front inequality and inclusiveness measures; it reveals an unclear path to data ownership and privacy; it provides predictions which are not clearly translated into pedagogical actions; and the possibility of self-regulated learning and game-based learning are not capitalized upon. However, as learning analytics research progresses, greater opportunities lie ahead, and a better integration between information science and learning sciences can bring added value of learning analytics dashboards in education.
... According to the 2017 Horizon K-12 report [12], the main goals of using LA in primary and secondary education are to predict student outcomes, implement interventions or adapt the curriculum, and even suggest new approaches to improve student success. This is corroborated by recent reviews for which LADs seem to provide efficient predictive models [13] for retention, study success, test score, drop-out and students' well-being [14,15]. The COVID-19 pandemic functioned as a catalyst for an exponential growth in the use of digital technology in classrooms, and the creation of data from this; however it is not clear that teachers are able to capitalize on this data and there is, therefore, a need to develop LADs for improving pedagogical decision-making. ...
Background
The enhancement of–or even a shift from–traditional teaching and learning processes to corresponding digital practices has been rapidly occurring during the last two decades. The evidence of this ongoing change is still modest or even weak. However, the adaptation of implementation science in educational settings, a research approach which arose in the healthcare field, offers promising results for systematic and sustained improvements in schools. The aim of this study is to understand how the systematic professional development of teachers and schools principals (the intervention) to use digital learning materials and learning analytics dashboards (the innovations) could allow for innovative and lasting impacts in terms of a sustained implementation strategy, improved teaching practices and student outcomes, as well as evidence-based design of digital learning material and learning analytics dashboards.
Methods
This longitudinal study uses a quasi-experimental cluster design with schools as the unit. The researchers will enroll gradually 145 experimental schools in the study. In the experimental schools the research team will form a School Team, consisting of teachers/learning-technologists, school principals, and researchers, to support teachers’ use of the innovations, with student achievement as the dependent variable. For the experimental schools, the intervention is based on the four longitudinal stages comprising the Active Implementation Framework. With an anticipated student sample of about 13,000 students in grades 1–9, student outcomes data are going to be analyzed using hierarchical linear models.
Discussion
The project seeks to address a pronounced need for favorable conditions for children’s learning supported by a specific implementation framework targeting teachers, and to contribute with knowledge about the promotion of improved teaching practices and student outcomes. The project will build capacity using implementation of educational technology in Swedish educational settings.
... LA has been applied and utilized in laboratory-based disciplines (natural sciences, biology, and chemistry) [69], and professors have implemented it in diverse settings (primarily technical), such as the prediction of underachieving students, automated feedback, development of strategies for optimal learning, pedagogical support, trustworthy peer assessment [70], and facilitating effective teamwork and collaboration [71][72][73][74]. The integration of AI with LA opens a wide range of educational opportunities supporting the personalization of learning, adaptive designs for instructions, and learning-processorientation optimization [75][76][77][78]. ...
The application of the Internet of Things is increasing in momentum as advances in artificial intelligence exponentially increase its integration. This has caused continuous shifts in the Internet of Things paradigm with increasing levels of complexity. Consequently, researchers, practitioners, and governments continue facing evolving challenges, making it more difficult to adapt. This is especially true in the education sector, which is the focus of this article. The overall purpose of this study is to explore the application of IoT and artificial intelligence in education and, more specifically, learning. Our methodology follows four research questions. We first report the results of a systematic literature review on the Internet of Intelligence of Things (IoIT) in education. Secondly, we develop a corresponding conceptual model, followed thirdly by an exploratory pilot survey conducted on a group of educators from around the world to get insights on their knowledge and use of the Internet of Things in their classroom, thereby providing a better understanding of issues, such as knowledge, use, and their readiness to integrate IoIT. We finally present the application of the IoITE conceptual model in teaching and learning through four use cases. Our review of publications shows that research in the IoITE is scarce. This is even more so if we consider its application to learning. Analysis of the survey results finds that educators, in general, are lacking in their readiness to innovate with the Internet of Things in learning. Use cases highlight IoITE possibilities and its potential to explore and exploit. Challenges are identified and discussed.
