In recent decades, a number of training environments have moved toward program approaches targeting the development of competencies. Because of their complexity, monitoring the development of those competencies is a considerable challenge. Our hypothesis is that a computerized system could help overcome this challenge if it is well accepted by its users. We first summarize the context surrounding the implementation of such approaches. Next, we present a computerized assessment system established in the Family Medicine Residency Program of Laval University (Québec, Canada) that we have developed for tracking the development of residents’ competencies. We then present the analysis of interactions between the system and users and the various proposals that were made to improve the system and longitudinal tracking of the development of the targeted competencies. We consider that this research provides useful guidelines for the computerized monitoring of learners' competencies development and for the design of such systems.

In lectures with presentation slides such as an e-learning lecture on video, it is important for lecturers to control their non-verbal behavior involving gaze, gesture, and paralanguage. However, it is not so easy even for well-experienced lecturers to properly use non-verbal behavior in their lecture to promote learners’ understanding. This paper proposes robot lecture, in which a robot substitutes for human lecturers, and reconstructs their non-verbal behavior to enhance their lecture. Towards such reconstruction, we have designed a model of non-verbal behavior in lecture. This paper also demonstrates a robot lecture system that appropriately reproduces non-verbal behavior of human lecturers with reconstructed one. In addition, this paper reports a case study involving 36 participants with the system, whose purpose was to ascertain whether robot lecture with reconstruction could be more effective for controlling learners' attention and more beneficial for understanding the lecture contents than video lecture by human and robot lecture with simple reproduction. The results of the case study with the system suggest the effect of promoting learners’ understanding of lecture contents, the necessity of reconstructing non-verbal behavior, and the validity of the non-verbal behavior model.

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.

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-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.

This research, to design and develop a concept map authoring support tool, adopts a semi-automatic concept mapping approach to help teachers create concept maps from English readings. A concept map is widely regarded as a useful teaching and learning tool. It offers many potential advantages apart from representing the students’ knowledge and understanding during learning. Students’ engagement in using and creating concept maps with a computer-enabled concept mapping tool raises concept maps’ potential benefits. It contributes to the learning process and improves the students’ meaningful learning. The Kit-Build concept map framework, which incorporates a technology-enabled concept mapping tool, uses concept map recomposition as its essential learning activity. In learning with Kit-Build, teachers compose concept maps that they want the students to achieve. The teachers’ maps are then decomposed into components from which the students recompose and reflect deeply on their understanding. The difference between teacher’s and students’ concept maps depicts the gap between teachers’ expected understanding and students’ actual understanding. Hence, the teachers’ concept map becomes an essential part of learning with Kit-Build. For some teachers, creating a good concept map for learning is difficult and time-consuming. Hence, support to improve teachers’ productivity in creating concept maps is essential. The findings suggest that the support tool yields better concept mapping efficiency while maintaining concept maps of similar quality. Teachers also found that the support tool was useful. Therefore, semi-automatic concept mapping with the supported Kit-Build concept map authoring tool has been shown to be a better approach.

Writing as a multiple-step process is one of the most complex and demanding skills for graduate students to master. Foreign or second language learners who are required to write for academic purposes at the university level may even find it more demanding to master. One of the ways of decreasing the burden of mastering this skill for learners is mediation, using scaffolding techniques to teach writing. Hence, having a good understanding of the impact(s) of adopting mediating or scaffolding techniques in writing classes is absolutely indispensable. To this end, the present study employed an experimental research design to investigate the impact of mediation in the flipped writing classrooms of the students of medicine. To peruse this goal, 47 medical students were selected through purposive sampling and put into control and treatment groups. Medical students in the treatment group watched teacher-made video content(s) before their writing classes. The students in this group experienced organized-interactive writing group activities in their classes. Unlike the experimental group, the students in the control group received all the instructions in the classroom and were assigned homework. The findings obtained through the ANOVA and t-test indicated that the students in the experimental group significantly outperformed their counterparts in the control group in terms of their writing. A probable conclusion could be that by requiring students to study in advance and take responsibility for their learning, flipped classroom can provide the opportunity for learners to actively construct knowledge rather than receive the information passively in the classroom. Flipped classroom can also cultivate interactive class time for teachers and enable them to invest in more fruitful academic practices, instead of asking students to spend a substantial amount of time each week doing homework independently.

When we translate Japanese sentence into English, sometimes several English words become the candidates. However, the usage situation of these candidate words is not the same. In order to choose appropriate words from them, we need to understand the usage situation for each candidate words. Usage situation of the words can be inferred by co-existing words in their example sentences. Co-existing words in example sentences are not always the same, so in order to understand usage situation, we need to generalize co-existing words from several example sentences. However, some of us who do not consciously generalize the co-existing words do not acquire the usage situation. This paper proposes the system which provides the environment where we can explicitly generalize co-existing words (keywords) in the example sentences to acquire the usage situation of the target words. This system also has a generalization support mechanism to provide concepts of words acquired through WordNet as hints. According to the experimental results, participants who used the system in learning English words reduced the number of incorrectly choosing the words and promoted to derive the own understanding of the usage situation.

Research has demonstrated that people generally think both their knowledge and performance levels are greater than they are. Although several studies have suggested that knowledge and progress visualization offered by open learner modeling (OLM) technology might influence students’ self-awareness in a positive way, insufficient evidence exists to show that this is the case. This paper examines the effects of open learner modeling and its extension with social comparison features, known as open social learner modeling (OSLM), on students’ knowledge monitoring abilities. We report the results of two semester-long classroom studies, using subjects who were undergraduate and graduate students in Java Programming and Database Management courses at the University of Pittsburgh. During their studies, the students were able to use different versions of an online practice system equipped with both OLM and OSLM. The students’ knowledge monitoring abilities were examined in two ways: through absolute and relative assessments. According to the results, although in both OLM and OSLM groups the students’ absolute knowledge monitoring ability increased during the semester-long study, relative self-assessment ability (i.e., their ability to compare their own knowledge levels with the knowledge levels of their peers) only increased in the OSLM group. The authors also traced relationships between the students’ academic achievement and their absolute and relative knowledge monitoring abilities.

High attrition and dropout rates are common in introductory programming courses. One of the reasons students drop out is loss of motivation due to the lack of feedback and proper assessment of their progress. Hence, a process-oriented approach is needed in assessing programming progress, which entails examining and measuring students’ compilation behaviors and source codes. This paper reviews the elements of a process-oriented approach including previous studies that have used this approach. Specific metrics covered are Jadud’s Error Quotient, the Watwin Score, Probabilistic Distance to Solution, Normalized Programming State Model, and the Repeated Error Density.