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This study investigates learner’s reading behaviors in a critical reading task in humanities course using learning analytics techniques. A Critical Analysis of Literature and Cinema course was selected as a context. The course activities evolved over 10 years, and for this instance, some face-to-face classroom critical reading activities were migrated to online mode by using BookRoll, a learning analytics enhanced eBook platform. Students (n=22 out of the 50 registered) accessed Hayavadana, an Indian play uploaded on BookRoll, and attempted to identify performative elements and cultural references in the text and highlight them. In this study, we analyze learner’s reading logs gathered in the learning record store linked to BookRoll during that activity. We extend our previous work where we identify four online reading profiles: effortful, strategic, wanderers, and check-out, based on learner’s clickstream interactions and time spent with the content. We validate the profiles with qualitative interview data collected from the learners and illustrate the quantified learning behaviors of each of those profiles based on an engagement metric. Our work aims to initiate further discussion related to the application of learning analytics in humanities courses both to probe into the learning behaviors of the students and thereby enhance the experiences with the use of interactive learning environments and data-driven services.

Self-directed learning (SDL) ability, its usefulness in higher education and life-long learning have been highlighted in previous literature. However, there has been much less understanding of the ...

UNESCO reported in March 2020 that 84.5% of total enrolled students are affected in some way by COVID-19 pandemic with more than 166 country-wide closures of schools (UNESCO, 2020). Japan too imposed emergency remote teaching and learning at both the school and university level. In this study, we focus on a national university in Japan, and investigate how teaching and learning were facilitated during this pandemic period using an e-book platform, BookRoll. BookRoll was linked as an external tool to the university’s learning management system. Such an endeavor also reinforced the Japanese national thrust regarding explorations of e-book-based technologies and using Artificial Intelligence (AI) in education. Teachers could upload reading materials, for instance their course notes, and associate an audio of their lecture. When students who registered in their courses accessed the learning materials, the system collected their interaction logs in a learning record store. Across the spring semesters from April to July 2020, the BookRoll system collected nearly 1.5 million reading interaction logs from more than 6,300 students across 243 courses in six domains with above 1,900 learners creating more than 78,000 reading logs weekly. While science and engineering courses mainly used the platform, law and language studies courses utilized the feature to upload audio tracks with reading materials. The interaction log analysis of the students with the content revealed that active learning actions, such as the use of annotations on text, were done more in humanities courses. Lastly, recommendations are created based on the analysis and the perception of the teachers about the emergency remote teaching and learning using the BookRoll system for orchestrating their course. © 2021, Canadian Network for Innovation in Education. All rights reserved.

Early prediction systems have already been applied successfully in various educational contexts. In this study, we investigated developing an early prediction system in the context of eBook-based teaching-learning and used students’ eBook reading data to develop an early warning system for students at-risk of academic failure -students whose academic performance is low. To determine the best performing model and optimum time for possible interventions we created prediction models by using 13 prediction algorithms with the data from different weeks of the course. We also tested effects of data transformation on prediction models. 10-fold cross-validation was used for all prediction models. Accuracy and Kappa metrics were used to compare the performance of the models. Our results revealed that in a sixteen-week long course all models reached their highest performance with the data from the 15th week. On the other hand, starting from the 3rd week, the models classified low and high performing students with an accuracy of over 79%. In terms of algorithms, Random Forest (RF) outperformed other algorithms when raw data were used, however, with the transformed data J48 algorithm performed better. When categorical data were used, Naive Bayes (NB) outperformed other algorithms. Results also indicated that models with transformed data performed lower than the models created using categorical data. However, models with categorical data showed similar performance with models with raw data. The implications of the results presented in this research were also discussed with respect to the field of Learning Analytics.

Self-direction skill is considered a vital skill for twenty-first-century learners in both the learning context and physical activity context. Analysis skill for self-directed activities requires the students to analyze their own activity data for understanding their status in that activity. It is an important phase that determines whether an appropriate plan can be set or not. This research presents a framework designed to foster students’ analysis skill in self-directed activities, which aims (1) to build a technology-enabled learning system allowing students to practice analyzing data from their own daily contexts, (2) to propose an approach to model student’s analysis skill acquisition level and process, and (3) to provide automated support and feedback for analysis skill development tasks. The analysis module based on the proposed framework was implemented in the GOAL system which synchronized data from learners’ physical and reading activities. A study was conducted with 51 undergraduate students to find reliable indicators for the model to then measure students’ analysis skills. By further analyzing students’ actual usage of the GOAL system, we found the actual activity levels and their preferences regarding analysis varied for the chosen contexts (learning and physical activity). The different context preference groups were almost equal, highlighting the utility of a system that integrates data from multiple contexts. Such a system can potentially respond to students’ individual preferences to execute and acquire self-direction skill.

Jigsaw learning is one of the popular cooperative learning methods which has been utilized in many English as Foreign Language (EFL) classrooms. In recent years, utilization of technology in education has accelerated. In this study, Jigsaw+, a new jigsaw learning approach with an e-book reader, was proposed to explore how the learning design can be applied for English vocabulary acquisition, reading comprehension, and students’ engagement in group activities. A quasi-experimental study was conducted at a high school in Japan for advanced and standard class students. Participants read and comprehend a story in English through BookRoll, an e-book platform with an analysis tool embedded. Two research questions were investigated; 1) to what extent did Jigsaw+ affect students’ English vocabulary knowledge and reading comprehension? and 2) to what extent did Jigsaw+ promote students’ reading engagement in the jigsaw group activities? The results revealed that Jigsaw+ learning tasks assisted to improve students’ vocabulary and reading comprehension for both the advanced and standard groups. Moreover, it facilitated students’ engagements in activities. Some limitations of the study are also discussed.

Self-regulation in learning involves planning and utilizing different shared resources. This study investigates learning strategies of different student groups when they are accessing course materials in digital medium – one group is the students with high academic performance, the other is the students with low academic performance. We analyze data of 116 students from a mathematics course in a junior high school. Using the differential pattern mining technique, we highlight underlying course content accessing patterns from the learning log collected by an e-book system, BookRoll.

In this paper, we propose an interactive drawing based activity in BookRoll, an e-book-based teaching-learning platform. Participants who were researchers drew an overview of their research as a handwritten memo. We demonstrate the affordances of the learning analytics (LA) enhanced platform to facilitate practice and research of such a drawing activity focused on a higher-order thinking task to organize knowledge (research overview in this case). We share the multi-modal data of 4 participants to illustrate the outcomes of the activity and their perception of the activity. An initial analysis of the handwritten memo is visualised. Further reflections show how the use of learning analytics-based platforms can enrich the learning design of a knowledge organisation task.

In recent years, machine learning of increasing complexity is being applied to problems in education. However, there is an increasing call for transparency and understanding into how the results of complex models are derived, leading to explainable AI gaining attention. The application of machine learning to automated group formation for collaborative work from learning system logs and other data has been progressing. Building on previous research in this field, we propose a group formation method that is based on a combination of course knowledge structures, reading behavior, and assessment analysis to create optimal heterogenous and homogeneous working groups using a genetic algorithm. The characteristics of each group are presented for explanation as a visualized knowledge map showing the strengths and weaknesses of each group, and are in the structure form of curriculum. We also present a case study of applying the method to junior high school mathematics log data, and provide explanation in a visualized form of standardized curriculum of group characteristics that are often referenced for learning design by teachers.

We aim to identify the engagement strategies that higher education students, engaging in emergency online learning in low-resource settings, perceive to be effective. We conducted a sequential mixed-methods study based on Moore&rsquo, s interaction framework for distance education. We administered a questionnaire to 313 students engaging in emergency online learning in low-resource settings to examine their perceptions of different engagement strategies. Our results showed that student&ndash, content engagement strategies, e.g., screen sharing, summaries, and class recordings, are perceived as the most effective, closely followed by student&ndash, teacher strategies, e.g., Q and A sessions and reminders. Student&ndash, student strategies, e.g., group chat and collaborative work, are perceived as the least effective. The perceived effectiveness of engagement strategies varies based on the students&rsquo, gender and technology access. To support instructors, instructional designers, and researchers, we propose a 10-level guide for engaging students during emergency online classes in low-resource settings.

Digitized learning materials are a core part of modern education and also can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open ebook test from the perspective of performance and identifying strategies that are used by both high and low performing learners. We create models to predict the performance of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that 1) strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment; and 2) low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment.

Self-directed learning (SDL) ability, its usefulness in higher education, and life-long learning have been highlighted in previous literature. However, understanding the effects of SDL ability in the school settings, specifically how it affects learners' affective and behavioral outcomes, remains missing and calls for further investigation. Given this research gap, this study developed a goal-oriented active learning system, GOAL, to support students' SDL and investigated how students' perceptions of SDL ability affect their reading engagement, SDL behavior, and motivation for extensive reading. The results showed that the high SDL ability students demonstrated significantly more reading engagement, SDL behaviors, motivation and autonomy for extensive reading than those with low SDL ability. These findings suggested that an SDL support environment could be exploited as a useful tool to support foreign language learning in the schools; however, the affective and behavioral outcomes created by the environment were affected to varying degrees by the levels of students’ SDL ability. The study provided implications for researchers studying extensive reading and SDL environments, as well as for educators seeking to improve extensive reading with SDL strategy usage.

For the 21st century learner, developing self-direction skill is crucial for both academic activities and maintaining one's healthy lifestyle. While there are technology supports for specific self-regulated learning tasks and health monitoring, research is limited on how to support development of meta-skill of self-direction process itself. In our work, we focus on designing seamless technology infrastructure to foster self-directedness of learners. We consider learning and physical activities data as a context and DAPER (data collection-analyze-plan-execution monitoring-reflect), as a data-driven self-direction skill execution and acquisition model. We bridge Learning Analytics and Quantified-Self approaches to develop the GOAL (Goal Oriented Active Learner) system to support synchronize-visualize-analyze multisource data regarding learners' learning and physical activities. This paper proposes a measurement rubric as a basis of adaptive scaffolding for skill development during the process.

Nanoswimmers are important because of their potential use for the purpose of drug delivery, monitoring, and diagnostics for in vivo biomedical application. They mimic microorganisms and mostly modeled as propelled by beating or rotating flagella. In the vast literature available on modeling of flagellar propulsion, the flagellum is considered constant diameter where as the actual microorganism have tapered flagella. The present study deals with the modeling and simulation of planar wave propagation through a tapered flagellum of a nanoswimmer for a given taper ratio. The performance parameters of velocity and efficiency are compared with the uniform diameter case. Taper diameter modeling of flagellum gives a superior performance by indicating higher velocity and efficiency. The parametric study with respect to the elasticity of the material of the flagellum and its beat frequency is also analyzed. The performance increases with increasing frequency, and the maxima for both velocity and efficiency of the tapered case is reached at a lower elasticity than the uniform case. The maximum efficiency of the taper case is almost double the maxima of the uniform diameter flagellum. The observations in the present study can be utilized while designing artificial flagellum for nanoswimmers and suggests that the tapered flagellum would be a more optimal choice of design as compared to the uniform one of the same volume.

Interactive Stratified Attribute Tracking (iSAT) is a visual analytics tool for cohort analysis. In this paper, we show how instructors can use iSAT to visualize transitions of groups of students during teaching-learning activities. Interactive visual analytics gives the instructor the affordance of understanding the dynamics of the class of students and their activities from the data collected in their own teaching-learning context. We take an example of a peer instruction (PI) activity and describe how iSAT can be used to analyze its clicker responses. During PI, typically instructors only use histograms to visualize the distribution of clicker responses in the pre- and post-discussion phases. We show that the use of iSAT to analyze clicker data in real time to trace transitions of participants’ responses during various voting phases can support them in planning for their post-PI activities. Seven patterns of transitions that emerge are aligned, returns, starburst, slide, attractor, switching, and void. We interpret them in the context of the example. Such transition patterns are neither available in multiple histograms of individual voting phase nor generated in real time to be visualized as a flow diagram. We had conducted two workshops to introduce iSAT to the instructors and demonstrated the workflow of using iSAT with their dataset. Here, we report usefulness and usability data collected from those workshops. In conclusion, we highlight the power of iSAT for instructors to do cohort analysis in their teaching-learning practice.

Engineering education research generates many insightful findings on learning and teaching of engineering concepts. These research findings need to be translated into practical implementations in the classroom to help students to achieve better learning outcomes. This paper describes a project by research scholars specializing in educational technologies. The aim of the project was to use the analyses of three phenomenographic studies to plan teaching and learning activities in engineering classrooms. The phenomenographic studies focused on the practice of engineering design across disciplines, sustainable design, and cross-disciplinary practice. The teaching-learning plans draw on curriculum, instructional, and technological innovations to facilitate understandings of engineering design identified in the phenomenographic studies. The paper contributes by relating theory to practice and demonstrates ways of utilizing research findings in the design of teaching-learning activities for engineering students.

Representational competence (RC), defined as "the ability to simultaneously process and integrate multiple external representations (MERs) in a domain", is a marker of expertise in science and engineering. However, the cognitive mechanisms underlying this ability and how this ability develops in learners, is poorly understood. In this paper, we report a fully controllable interface, designed to help school students develop RC. Further, as the design emerged from the application of distributed and embodied cognition theory to the RC problem, the design also seeks to shed light on the cognitive mechanisms underlying the integration of MERs. Here we report a preliminary eye and mouse tracking study, which sought to develop a detailed understanding of how students interacted with our interface, under self and text-guided exploration conditions. We also examined how the interaction process related to students' ability to integrate the representations in the interface. Results highlighted several desirable student behaviors, and potential points of modification of the interface to improve integration of MERs.

Interactive Stratified Attribute Tracking Diagram (iSAT) is a data visualization and tool to assist interactive visual analytics of multi-attribute learning dataset. The present work reports the evolution of this diagram through a design based research methodology following its three design iterations. There are two output at the current stage i) iSAT and its web-based interaction. ii) A Learning Analytics method suitable for both researchers and practitioners to trace student attribute value. We received satisfactory user testing score for Pre-test and post-test marks visualization through a two-phase iSAT.

We have created a visual representation called Stratified Attribute Tracking (SAT) Diagram to explicate trends that are otherwise implicit in learning analytics data. SAT Diagram is a unified graph that enables tracking individual attribute values in a dataset and stratifying them according to criteria set by the researcher. SAT diagram represents the transition of samples between strata across attributes. In this paper we introduce the SAT diagram and illustrate how to generate, interpret and analyze them. We believe the process of SAT diagram generation would enable exploring deeper research questions on learning data.

Educational researchers use observations to collect information about classroom activities as they occur. Currently the most prevalent methods of logging classroom observations are pen-and-paper or video recording. The former is inefficient, especially in large classes while the latter requires expensive infrastructure. In this paper we propose the design framework for the development of a software application for protocol-based multi-observer data logging and analysis called PULSE. Following design-based research we developed the four aspects of this framework. They were iteratively improved over the research cycles based on the feedback of the previous cycles. In this work-in progress paper, we present an ongoing design prototype of the application.