Students’ affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students’ affective states are determinative of the learning quality. However, measuring various affective states and what influences them is exceedingly challenging for the lecturer without having real interaction with the students. Existing studies primarily use self-reported data to understand students’ affective states, while this paper presents a novel learning analytics system called MOEMO (Motion and Emotion) that could measure online learners’ affective states of engagement and concentration using emotion data. Therefore, the novelty of this research is to visualize online learners’ affective states on lecturers’ screens in real-time using an automated emotion detection process. In real-time and offline, the system extracts emotion data by analyzing facial features from the lecture videos captured by the typical built-in web camera of a laptop computer. The system determines online learners’ five types of engagement (“strong engagement”, “high engagement”, “medium engagement”, “low engagement”, and “disengagement”) and two types of concentration levels (“focused” and “distracted”). Furthermore, the dashboard is designed to provide insight into students’ emotional states, the clusters of engaged and disengaged students’, assistance with intervention, create an after-class summary report, and configure the automation parameters to adapt to the study environment.

Active learning is a learner-centric instructional method that uses discussion, role play, collaborative problem-solving based approaches to engage students with the course materials. However, due to the pandemic, active learning activities take place over multiple learner-centric technologies, as classroom-centered activity design is no longer possible. This study explored the success stories of active learning in disadvantageous educational contexts, particularly in Arab regions. After examining the theory, models, various learner-centric technologies of pre-pandemic active learning de-signs, this study proposes 25 emerging technologies to support active learn-ing 19 active learning strategies in terms of activity design in new education normal. The three-fold findings are related to designing active learning activities in new education normal, enhancing less practiced active learning strategies, and bridging the gaps in pre-and post-pandemic active learning activity design using learner-centric technologies.

Contextual factors in which learning occurs are crucial aspects that learning analytics and related disciplines aim to understand for optimizing learning and the environments in which learning occurs. In foreign vocabulary development, taking the notes or memos of learning contexts along with other factors, play an essential role in quick memorization and reflection. However, conventional tools fail to automate the learning contexts generation process as learners still need to take memos or e-notes to describe their vocabulary learning contexts. This paper presents the Image Understanding Project (hereafter IUEcosystem) that could produce smartly-generated learning contexts primarily in a learner’s target languages. The IUEcosystem uses visual content analysis of lifelogging images as the sensor data to produce smartly-generated learning contexts that could be used as an alternative to handwritten memos or electronic notes. The IUEcosystem uses applied artificial intelligence to produce smartly-generated learning contexts. This intelligent learning environment collects a learner’s learning satisfaction and interaction data and, later on, analyzes them to produce time-based notifications for enhancing retention. Furthermore, a new learning design is presented that aims to map a learner’s prior vocabulary knowledge with new learning vocabularies to be learned. This learning design would help learners to review and recall prior knowledge while learning new vocabulary.

This paper describes an automatic quiz generation system designed to support language learning that utilizes digital textbook logs. Learners often memorize words in digital textbooks while preparing for an examination, and they often use the highlight function for the words. Previous studies regarding annotations and highlights have shown that learning only by using the highlight function on important content in textbooks did not affect learning achievements. Therefore, in this study, we developed a system that can support the repeated learning by analyzing digital textbook logs and providing appropriate quizzes. An evaluation experiment involving 31 international students was conducted to assess whether the quizzes provided by our proposed system are able to enhance the learning achievements as compared to teacher-created quizzes. The results show that the quizzes by our proposed system and the teacher-created quizzes were both equally effective. A correlation analysis was conducted to identify the correlation among the learning achievements, the number of quizzes, and each variable in questionnaires. We found that there is a positive correlation between the number of quizzes and the students’ learning achievements.

In ubiquitous learning, authentic experiences are captured and later reused as those are rich resources for foreign vocabulary development. This article presents an experiential theory-oriented approach to the design of learning analytics support for sharing and reusing authentic experiences. In this regard, first, a conceptual framework to support vocabulary learning using learners' authentic experiences is proposed. Next, learning experiences are captured using a context-aware ubiquitous learning system. Finally, grounded in the theoretical framework, the development of a web-based tool called learn from others (LFO) panel is presented. The LFO panel analyzes various learning logs (authentic, partially-authentic, and words) using the profiling method while determining the top-five learning partners inside a seamless learning analytics platform. This article contributes to the research in the area of theory-oriented design of learning analytics for vocabulary learning through authentic activities and focuses on closing the loops of experiential learning using learning analytics cycles.

Learning analytics dashboards (LADs) have an important role in delivering the analytics results to the stakeholders. Student-facing LADs are frequently used in online learning environments to support students’ self-regulation processes and motivation. However, to make dashboards effective, students are expected to visit them regularly. Studies showed that despite their benefits, students might not have enough motivation to use LADs. Gamification is commonly used in educational environments to increase students’ motivation. This chapter discusses the development of a gamified LAD and its evaluation from the perspective of the Gamification Acceptance Model. Students’ dashboard interactions were also analyzed to understand their actual usage behaviors. An evaluation of the LAD is carried out with 64 participants enrolled in a Computer Hardware course offered for sophomores. The course was taught remotely using distance learning technologies. Live sessions were held once a week, and students were required to complete weekly tasks using a learning management system. Results of the evaluation study indicated 95% of the students visited the dashboard multiple times throughout the semester. In addition, students found the dashboard useful and easy to use and contributed to their learning and stated they would like to use it in other courses.