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The development of mobile Internet, Internet of Things, and cloud computing has contributed to the unprecedented growth of information data. Big data plays a very important role in education. Currently, the literature review and in-depth research on big educational data are not very extensive, mainly involved in two fields: education mining and learning analysis. For a perfect research about education big data, this paper comprehensively reviewed three major aspects (Predictive Analytics, Learning Analytics, and Recommendation Systems) of educational data analytics for an intensive investigation and analysis: (1) Predictive Analytics: It predicts students’ learning performance by tracking students’ learning information and then analyzes students’ learning competence to build an academic early warning system; teachers can be allowed to intervene in students in time and adopts different teaching ways for different students. Therefore, both students’ learning and ability can be individualized and improved; (2) Learning Analytics: This part can identify the learners’ behavior patterns and obtain more implicit learner characteristics by studying the hidden meaning behind learning behaviors and strategies; (3) Recommendation Systems: It can match the needs of learners and recommend appropriate learning resources through different methods. All the above proved that the application of big data technology in education provides powerful data support for the development of education.

In the context of learning analytics, machine learning techniques have been commonly used in order to shed a light on solving educational problems. The studies can be associated with the curriculum design, mostly at course level, which target to improve the learning and teaching processes. In this study, the relationship between the courses was analyzed via artificial neural network (ANN) to provide a support for curriculum development process at a program level. Extracting the dependence among courses within a program plays a key role in placing them coherently to ensure the success of curriculum. For this purpose, it was investigated if the performance of students in a subsequent course is influenced from the performance in some previous basic courses. The results demonstrated that the performance relations could be used to describe information for prioritization and sequencing of courses within a program. In addition, ANN can successfully predict the student performances leading to find out the relationships between these courses. The application of the multilayer feedforward neural network resulted in an achievement of a prospering prediction performance based on the grades of prerequisite courses with 87% accuracy rate without sacrificing a significant sensitivity.

Socially, politically, and morally, the world of sport is still changing. On the other hand, technology has been the most prevalent transition in the sport over the last century. Thanks to modern science, athletes can now go higher, run quicker, and, most importantly, remain healthy. Although academics, agencies, and policymakers had already urged physical education teachers to use technology in their classrooms, in many of these situations, technology is used for administrative purposes, including tracking enrolment and measuring, documenting, and reporting students’ work. Thus, this paper suggests an intelligent Student Actions Evaluation System using Deep Learning (iSAES-DL)for student monitoring in physical education. This model uses the deep convolution neural network for the classification of risky actions. This model further evaluates the learners’ degree of learning, retention, and achievements and suggests improvements and corrective measures. It highlights the benefits, uses, and limitations of applying deep learning techniques and IoT devices to develop learning analytics systems in the physical, educational domain. Eventually, output criteria such as comprehension, concentration, retention, and learner attainment are given a feature-by-feature analysis of the proposed methodology and traditional teaching-learning approaches. Finally, the classification algorithm is contrasted to other deep learning algorithms with an F1-score of 97.86%.

The aim of the study is to determine the impact of online learning activities on learning outcomes of students who participated in the blended learning course, focusing specifically on skill-based courses. The learning outcomes or results of a learner are usually measured by scores, knowledge or skills gained in the course. In blended learning courses, the learning outcomes can be assessed according to many criteria. In this study, interactive activities such as teacher–student interaction, student–student interaction, student–content interaction and student–technology interaction are considered. Undergraduate students participated in the blended learning course in which formative assessment was used to evaluate student learning outcomes by the combination of different learning activities through a learning management system. The quantitative results obtained by using regression analysis of data from the system showed that the students who effectively interacted with learning activities in the course have better results. Quantitative analytical results indicated that student–student interaction has a greater impact on student learning outcomes. These learning activities are used for interactive activities as suggestions for teachers to design and implement learning activities for blended learning courses.

Observing student body gesture has been widely used to assess teaching effectiveness over the past few decades. However, manual observation is not suitable for the automatic data analysis in the field of learning analytics. Consequently, a student body gesture recognition method based on Fisher Broad Learning System (FBLS) and Local Log-Euclidean Multivariate Gaussian (L²EMG) is proposed in this paper. FBLS is designed by introducing the discriminative information into the hidden layer of Broad Learning System (BLS) and reducing the dimensionality of hidden-layer representations. FBLS has superiorities in accuracy and speed. In addition, L²EMG, which is a highly distinctive descriptor, characterizes the local image with a multivariate Gaussian distribution. So L²EMG features are fed into the FBLS for recognition in this paper. Extensive experimental results on self-built dataset show that the proposed student body gesture recognition method obtains better results than other benchmarking methods.

Learning analytics, as a rapidly evolving field, offers an encouraging approach with the aim of understanding, optimizing and enhancing learning process. Learners have the capabilities to interact with the learning analytics system through adequate user interface. Such systems enables various features such as learning recommendations, visualizations, reminders, rating and self-assessments possibilities. This paper proposes a framework for learning analytics aimed to improve personalized learning environments, encouraging the learner’s skills to monitor, adapt, and improve their own learning. It is an attempt to articulate the characterizing properties that reveals the association between learning analytics and personalized learning environment. In order to verify data analysis approaches and to determine the validity and accuracy of a learning analytics, and its corresponding to learning profiles, a case study was performed. The findings indicate that educational data for learning analytics are context specific and variables carry different meanings and can have different implications on learning success prediction.

This paper addresses the importance of independent learning and how to educate people to a world where innovation is becoming more central and related to digital technologies. The five socio-emotional factors, associated with the metacognitive skills, which directly interfere in the learning processes, are described. In particular, the Learning Analytics is emphasized as the methodology that enables educators to make decisions by taking into account systematic and elaborate analyses of learners’ data and the educational contexts in which learning develops. The set of characteristics referring to the educational demands associated with the so-called “shallow works” are presented, as well as the main characteristics associated to the “deep works”. As a main conclusion, in this panorama in which innovation is strategic to meet the new educational requirements, learning how to learn is demonstrated to become more important than “just” learning.

Learning analytics is the measurement of student progress by the collecting, analysis and reporting data in the learning environment. Learning analytics methods try to find out the dependent pattern in dataset gathered. Learning Analytics improve student outcomes in several ways, first, using learning analytics leads to measure student success correctly. With this information students can find accurate teaching techniques and support themselves. Also, it gives proper and faster feedback about learning technique to the stakeholders (principals, teachers, parents). The scope and the aim of learning analytic projects may differ for different organizations. Selecting the right learning analytic project is crucial for the overall success of the learning process. Despite their benefits, while selecting the learning analytic projects not only financial benefits but also various factors including Privacy, Access, Transparency, Security, Accuracy, Restrictions, and Ownership should be taken into account. Yet, evaluating these factors is not easy since they involve uncertainties. Therefore, the selection of learning analytic projects is a complex process that includes various uncertainties. In this study, we utilize an Spherical fuzzy TOPSIS approach for selecting Learning Analytics Projects. This method enabled us defining the uncertainties with independent parameters.

Teaching and learning in the digital era refers to the use of digital technologies to support and enhance teaching and learning activities. Advancement in digital technologies has revolutionized the way teaching and learning are conducted, making education more accessible, interactive, and personalized. Digitalization of higher education systems has been considered a powerful means to promote student learning. Digital technologies can positively affect student learning in higher education and it becomes effective when teachers use it to promote student involvement in constructive and interactive as opposed to passive and traditional learning methods (Wekerle et al., 2022). Embracing technology in education is crucial for keeping up with the rapidly evolving world and preparing students for the future. This chapter provides an overview of teaching and learning in the digital era, describes the definition, types, and elements of teaching and learning in the digital era, explains the opportunities, and challenges of teaching and learning, and points out emerging patterns and provides insight into future trajectories how the use of digital technologies while providing the required support to enhance teaching and learning activities.

The survival and economic equilibrium of Higher Education institutions are heavily contingent on student retention. Predictive models for retention play a crucial role in identifying students at risk of attrition during the early phases, thereby aiding the financial sustainability of these educational establishments. In recent times, scholars have employed a range of data sources, including robust ones like the Learning Management System (LMS), which are meticulously examined and incorporated into these models by researchers to mitigate the perils of attrition.

Student engagement is an important factor toward academic success, and machine learning techniques are valuable tools for predicting and improving it. This chapter explores some applications of machine learning to predict student engagement by using different data sources, particularly in the context of online education. The chapter discusses different machine learning algorithms, including, logistic regression, decision trees, support vector machines, Naïve Bayes, deep learning, K-nearest neighbors, and random forests. It also dives into the importance of student engagement, the challenges involved, and the utilization of learning analytics in the educational process. The findings highlight the potential of machine learning in identifying at-risk students and optimizing educational outcomes in online learning environments.

While Learning Analytics has been widely used to improve learning experiences such as course content, activities, and assessments, it plays a significant role in providing data-driven insight into the efficacy of a program. This chapter sheds light on how big data can be used in curriculum development to ensure that the skills and competencies students learn at educational institutions align with those required in the current and future job market. By exploring three case studies in which big data has been utilized to revise and update curricula in different fields, this chapter suggests that big data allows curriculum designers to make data-driven decisions which leads to a higher rate of employability and satisfaction among students. This chapter also discusses the limitations and challenges of using big data in education.

When students are asked to examine their understanding individually or in small groups, information can become part of a feedback process that supports students’ learning. As designers of technology to support learning, we are interested in supporting such feedback processes in the context of guided inquiry instruction. This paper explores the potential of automatically associating mathematical descriptions with student submissions created with interactive diagrams. The paper focuses on the feedback processes that occur when students use the descriptions provided by the technology as resources for reflection and learning. We discuss the design of personal feedback processes where students reflect on and communicate their own learning, utilizing individually-reported multi-dimensional automatic analysis of their submissions in response to example-eliciting tasks. While there is much research and development work to be done, we consider mathematical descriptions of student work as an important contribution to broader developments in learning analytics.