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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.

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.

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.

With COVID-19 pandemic forcing academic institutions to shift to emergency remote teaching (ERT), teachers worldwide are attempting several strategies to engage their learners. Even though existing research in online learning suggests that effectiveness of the online session is more dependent on pedagogical design rather than technology feature, teachers may still focus on the intricacies of the technology. In this paper, we present the evolution of an active learning pedagogy, supported by technology (eBook reader—BookRoll, Analytics Dashboard—LAViEW), for an undergraduate physics classroom across a semester that was affected by the lockdown due to pandemic. The technology-enhanced pedagogy evolved in three phases—technology used in “Content Focus” mode, technology used in “Problem Focus” mode and technology used in “Learning Dialogue Focus” mode. The entire activities were designed and implemented within the technology-enhanced and evidence-based education and learning (TEEL) ecosystem, which supported integration of learning technologies with analytics system. Comparison of the student’s learning logs indicated that there was a sustained engagement in the learning activities conducted during the blended (before lockdown) and online mode (during lockdown). We had conducted one-way ANOVA to compare the post-test scores for each teaching phase and found statistically significant differences in the latter phases. A preliminary qualitative analysis of the learner artifacts generated as memos in BookRoll during each phase revealed that students were posing conceptual clarifications during the latter phases. These were also having greater alignment with the session agenda and showed construction of new knowledge based on the seed knowledge provided during the instructor–learner interaction sessions. The study provides key insights into how reflection and practice by both learner and teacher improves the acceptance of technology-enabled pedagogy.

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.

Collaborative learning in the form of group work is becoming increasingly significant in education since interpersonal skills count in modern society. However, teachers often get overwhelmed by the logistics involved in conducting any group work. Valid support for executing and managing such activities in a timely and informed manner becomes imperative. This research introduces an intelligent system focusing on group formation which consists of a parameter setting module and the group member visualization panel where the results of the created group are shown to the user and can be graded. The system supports teachers by applying algorithms to actual learning log data thereby simplifying the group formation process and saving time for them. A pilot study in a primary school mathematics class proved to have a positive effect on students’ engagement and affections while participating in group activities based on the system-generated groups, thus providing empirical evidence to the practice of Computer-Supported Collaborative Learning (CSCL) systems.

The COVID-19 pandemic forced the transition to emergency online learning without prior preparation or guidelines. This transition has been particularly challenging in developing countries and low-resource contexts and hindered student engagement. We aim to identify the engagement strategies which students, engaging in emergency online learning in low-resource contexts, perceive to be effective. We conducted a sequential mixed-methods study based on Moore’s interaction framework for distance education. First, we conducted a literature review and interviewed ten teachers and ten students to identify a list of engagement strategies. Then, we designed a questionnaire that examines student perceptions of these strategies. We administered the questionnaire to 313 students engaging in emergency online learning in low-resource contexts. Our analysis results showed that student-content engagement strategies, e.g. screen sharing, summaries, and class recordings, are perceived as the most effective, closely followed by student-teacher strategies, e.g. Q&A sessions and reminders. Student-student strategies, e.g. group chat and collaborative work, are perceived as the least effective. The perceived effectiveness of engagement strategies depends on the context and the students’ characteristics, e.g. gender, major, and technology access. To support instructors, instructional designers, and researchers we propose a ten-level guide for engaging students during emergency online classes in low-resource contexts.

Self-direction skills in the context of learning can be supported with data in this digital era. This study analyzes the behaviors of learners during a self-directed reading and summarization task. Our work investigates an undergraduate course (n=72) where students worked on a reading and summarizing assignment while planning and monitoring the task in GOAL, a platform synthesizing learner’s activity data from learning and physical activity contexts. This study focuses on the initial cohort analysis of the students’ behavior based on the fine grain interaction data collected in the different systems using visual analytics techniques. Such a data-rich narrative of self-directed in-semester activities is not discussed yet in the literature to our knowledge. We discuss the implications of the trends that is found in our collected dataset for designing AI-support for self-direction skills with the GOAL platform and the scope of deeper analysis to further understand the process.

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.

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.

The current study is situated in a city where English is the second language in school. A two-group intervention was conducted in a kindergarten. The experimental group of learners was given computer-assisted intervention in phonological processing skill and the control group had traditional (pencil-andpaper) training on phonological processing skill. This paper reports the performance of those early learners across two phonological detection tests and explicates their performance patterns using the iSAT tool and its associated analysis method. The results showed that children in the experimental group outperformed than those in the control group during posttest and retention test which were assessed immediately after the intervention and 10 weeks after the intervention. This suggests the superior effect of using computer-assisted intervention on improving young learners' phonological detection ability.

MOOCs are increasingly being used to impart education at different levels. However, skill based courses are still in their early age. This paper presents a study done on a 3D animation MOOC. Learner data of performance in quizzes, perception of learning, and interest was gathered. Participants were grouped, accordingly to their prior exposure to online courses, as first time learners experienced learners. The effectiveness (9.1% completion rate) of the MOOC, was analyzed by comparing the perceptions of these two groups. First time learners perceived the contribution of the video towards their learning gain, more than the experienced learners. No difference in the perceived degree of contribution of specific aspects of the course like quizzes, assignments, and discussion forums was found.

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.

Localized drug delivery and nano manipulation in fluid media like blood is an important application in biomedicine. Nanoswimmers are potential drug delivery agents for inter-vascular and intra-cellular systems. The bio-mimic modeling of flagellar propulsion mechanism of nanoswimmers has been widely attempted in literature. Mathematical models of the flagellated nanoswimmers show dependence of motion on multiple parameters like geometry and material of flagella, and viscosity of the surrounding medium. The literature also provides constraints to the material property depending on the mimicked biological system. Although modeling of shape and size of nanoswimmers is widely investigated in the literature, the material selection for the flagella needs to be assessed on the criteria like biocompatibility, physical properties, and technological feasibility. The shortlisting is quintessential for attempts to engineer an artificial nanoswimmer. The present study provides a methodology for assessing the candidature of a material for the fabrication of artificial flagella, which shall have implant capabilities and be able to shortlist the potential material. Further screening criteria, based on biocompatibility and technology viability issues are also discussed.

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.

Muddy Points (MP) is a strategy to elicit and address individual students' doubts. While this can be effectively implemented in small classes, it is a challenge to do so in a large class. In this paper we propose LAMP, a framework for Large-scale Addressing of Muddy Points, as a mechanism for instructors to ensure that every individual student's doubts are addressed even in large classes. LAMP has three phases: Collection, Addressal, and Closure. In the collection phase, MPs are systematically collected through four different modes. In the addressal phase, MPs are categorized into six categories and addressed accordingly. In the closure phase, the discussions on MPs are summarized. We investigated the effectiveness of LAMP in an introductory computer science course having 450 students. We found that 68% of students confirmed they were able to pose their questions and 57% of students confirmed that there was closure to their questions.