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The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning analytics and Big Data in the Saudi Arabian higher education. Examining learning analytics in higher education institutions promise transforming the learning experience to maximize students' learning potential. With the thousands of students' transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics in Saudi Arabia has caught the interest of scholars and researchers regionally and internationally. This chapter explores a Saudi private university in Jeddah, Saudi Arabia, and examines its rich learning analytics and discovers the knowledge behind it. More than 300,000 records of LMS analytical data were collected from a consecutive 4-year historic data. Romero, Ventura, and Garcia (2008) educational data mining process was applied to collect and analyze the analytical reports. Statistical and trend analysis were applied to examine and interpret the collected data. The study has also collected lecturers' testimonies to support the collected analytical data. The study revealed a transformative pedagogy that impact course instructional design and students' engagement.

Recent advancement in technological development has encouraged distance learning institutions to be more productive and creative in effectively utilizing the Learning Management System (LMS). Among the many measures employed is the integration of federated search engine into the LMS which allows for a more productive and wider scope of information retrieval through the provisions of library resources and services. The purpose of this paper is to report one such case study in Wawasan Open University exploring the integration of federated search engine (EBSCO Discovery Service (EDS) widget) into the learning spaces of LMS. Widgets resemble apps that enable the integration of EDS functionality in providing access for students to retrieve library learning resources from the convenience of the LMS, excluding the need to log onto the library.

This paper presents a discussion that highlights the development and conjectural implementation of a framework on the integration of the EDS widget into the University’s LMS. Data collection includes meta-analysis data from the micro- and macro-level infrastructure that make up the framework, namely, end-user layer, system layer and data management layer.

Findings from this study addressed significant importance to the library in promoting effective search and utilization of information needs. The findings will also make clear recommendations in developing effective collaborations between the library and faculties. Although the implementation of this framework is still in a developmental stage, this study still provides pertinent information in validating the integration of EDS into the University’s LMS.

While serious limitations are not anticipated, possible concerns do exist with programming algorithms in the integration of EDS into the LMS. These challenges will be reported in the paper as reference for future replications of study

One key implication is the increase in the usage of the library resources and the potential to reach a larger audience of remote library users.

The primary advantage is to minimize the need for multiple gateway login while ensuring the library to monitor relevant library databases activities throughout the system check of the LMS.

Although multiple studies examine institutional transitions of learning management systems (LMS) or compare their merits, studies examining students' free choice of access on parallel LMSs for the same course are absent from the literature. In order to investigate usage in a free‐choice situation, identical content was posted at the same time to two different LMSs in a large enrollment class with a diversity of majors.

Two prevalent LMSs were utilized in the study: WebCT, which was in existence at a university‐wide level previously, and Moodle, which will become the new university‐wide system in the 2012‐13 academic year onwards. Both student self‐reports and LMS log usage data were analyzed. LMS preferences and usage groups were categorized.

Although this inquiry revealed that most students chose to use the WebCT system (85 per cent WebCT users, 15 per cent Moodle users; both self‐reported and log‐verified), the reasons given for WebCT preference pertained largely to habit and that most other courses are using the WebCT LMS. In contrast, the reasons given for using Moodle spoke directly to the attributes of the LMS itself, namely the interface quality and the way it is organized.

This study indicates that institutions should look beyond student usage patterns in making LMS choices, and that LMS quality is sometimes, and perhaps unfortunately, overshadowed by student habit and familiarity.

The study aimed to examine the relationship between self-regulation failure and academic achievement in an emergency remote teaching (ERT) and learning environment compared to a face-to-face setting.

This study conducted an analysis of covariance (ANCOVA) to investigate the impact of students falling behind (as proxy for self-regulation failure) on their final course mark. The sample comprised students from four undergraduate modules offered at a South African university in a face-to-face setting (N = 1,604), as well as an ERT setting (N = 1,478). Students falling behind were measured as the days behind, relative to the academic program, using learning management system (LMS) log data. The study further explored whether self-regulation failure had a greater effect on academic achievement in ERT as opposed to a face-to-face context.

The results indicated a negative correlation between self-regulation failure, evidenced by falling further behind in the academic program, and students' final course marks. Furthermore, the negative impact of falling behind was found to be greater on a student's final course mark during ERT compared to a face-to-face setting.

This study contributes to the literature on ERT by highlighting the increased negative effect of self-regulation failure on academic achievement in ERT as opposed to face-to-face teaching and learning. Findings of this research may be of value to educators and policymakers in identifying ways of supporting self-regulated learning in future ERT situations to ensure that academic success is maintained.

Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.

This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.

Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.

The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

The purpose of this paper is to propose a conceptual model of engagement, appropriated from social media marketing, as a sense-making framework to understand engagement as a measurable process through the development of engagement profiles. To explore its potential application to education the paper follows previous work with Personalised Learning strategies to place emphasis on the promotion of the learner voice – their ability to influence decisions affecting them and their community.

This paper will position engagement as a sociocultural process and adopt an Activity Theory based methodology demonstrated through a desk analysis of VLE data from a further education college.

The analysis suggests that the approach can yield insights that may be elusive in traditional measures reinforcing the overall conceptual proposal for a multi-method approach to profiling learner engagement.

The paper has focused on presentation and exploration of the conceptual approach, which has limited the scope to broaden the discussion of the desk analysis and wider findings that this approach reveals.

It is intended that the approach offers a generalizable model that can be adopted by institutions planning to measure engagement or develop learner activity profiles. Several areas of immediate potential are identified throughout the paper.

This paper contributes a multi-method approach to engagement as argued for in recent engagement literature. This should offer institutions a way to realise value from emerging ideas within related domains of Learning Design and Learning Analytics.

For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis.

This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach.

The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students.

The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

This study aims to investigate the futuristic visions of PhD students at Distance Education department of Anadolu University on the use of learning analytics (LA) and mobile technologies together.

This qualitative research study, designed in the single cross-section model, aimed to reveal futuristic visions of PhD students on the use of LA in mobile learning. In this respect, SCAMPER method, which is also known as a focused brainstorming technique, was used to collect data.

The findings of the study revealed that the use of LA in mobile can solve everyday problems ranging from health to education, enable personalized learning for each learner, offer a new type of evaluation and assessment and allow continuous feedback and feedforwards; yet this situation can also arise some ethical concerns since the big data collected can threaten the learners by interfering with their privacy, reaching their subconscious and manipulating them as well as the whole society by wars, mind games, political games, dictation and loss of humanity.

The research is limited with the views of six participants. Also, the sample of the study is homogeneous in terms of their backgrounds – their age range, their departments as PhD students and their fields of expertise.

The positive perceptions of PhD students provide a ground for the active use of LA in mobile. Further, big data collected through LA can help educators and system makers to identify patterns which will enable tailored education for all. Also, use of LA in mobile learning may stimulate the development of a new education system including a new type of evaluation and assessment and continuous feedback and feedforwards.

The widespread use of mobile technologies opens new possibilities for LA in the future. The originality of this research comes from its focus on this critical point.

Graduate students often come to statistics courses with varying levels of motivation and previous academic preparation. Within the statistics education literature, there is a growing consensus to guide instructors who want to help their students gain the requisite statistical knowledge so they can conduct their own research and report their results accurately. Recommendations from the literature include using real data, showing worked-out example problems, and providing immediate feedback to allow students to reflect on the correct and incorrect decisions they made in their analyses. This chapter describes the use of expert decision models (EDMs) in two graduate-level statistics courses – multiple regression and structural equation modeling. Decision-Based learning is an effective way to support graduate students’ developing thinking about statistics. In both courses, the students encounter the EDM through a series of assignments which guides students through the process of specifying a statistical model, running that model in Statistical Package for the Social Sciences or Mplus, and interpreting the results. These assignments use real datasets whenever possible and are designed to expose students to various issues they may experience in their research (missing data, violations of assumptions, etc.) and to illustrate how an expert would have adapted to those issues to complete the analysis. The EDM, with its just-in-time, just-enough instruction, helps students navigate these obstacles through guided practice and allows them to develop the conditional knowledge to handle issues that will arise as they carry out their own research.

This paper aims to investigate the relationship between the students’ digital activities and their academic performance through two stages. In the first stage, students’ digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the significance of the relationship between these profiles and the associated academic performance was tested statistically.

The LMS delivers E-learning courses and keeps track of the students’ activities. Investigating these students’ digital activities became a real challenge. The diversity of students’ involvement in the learning process was proven through the LMS which characterize students’ specific profiles. The Educational Data Mining (EDM) approach was used to discover students’ learning profiles and associated academic performances, where the activity log file exemplified their activities hosted in the LMS. The sample study data is from an undergraduate e-course hosted on the platform of Blackboard LMS offered at a Saudi University during the first semester of the 2019–2020 academic year. The chosen undergraduate course had 25 sections, and the students attending came from science, technology, engineering and math background.

Results show three clusters based on the digital activities of the students. The correlation test shows the statistical significance and proves the effect of the student’s profile on his academic performance. The data analysis shows that students with different profiles can still get similar academic performance using LMS.

This empirical study emphasizes the importance of the EDM approach using clustering techniques which can help the instructor understand how students use the provided LMS content to learn and then can deliver them the best educational experience.