Measuring Behaviors and Identifying Indicators of Self-Regulation in Computer-Assisted Language Learning Courses

The aim of this research is to measure self-regulated behavior and identify significant behavioral indicators in computer-assisted language learning courses. The behavioral measures were based on log data from 2454 freshman university students from Art and Science departments for 1 year. These measures reflected the degree of self-regulation, including anti-procrastination, irregularity of study interval, and pacing. Clustering analysis was conducted to identify typical patterns of learning pace, and hierarchical regression analysis was performed to examine significant behavioral indicators in the online course. The results of learning pace clustering analysis revealed that the final course point average in different clusters increased with the number of completed quizzes, and students who had procrastination behavior were more likely to achieve lower final course points. Furthermore, the number of completed quizzes and study interval irregularity were strong predictors of course performance in the regression model. It clearly indicated the importance of self-regulation skill, in particular completion of assigned tasks and regular learning

Direction of collaborative problem solving-based STEM learning by learning analytics approach.

The purpose of this study was to explore the factors that might affect learning performance and collaborative problem solving (CPS) awareness in science, technology, engineering, and mathematics (STEM) education. We collected and analyzed data on important factors in STEM education, including learning strategy and learning behaviors, and examined their interrelationships with learning performance and CPS awareness, respectively. Multiple data sources, including learning tests, questionnaire feedback, and learning logs, were collected and examined following a learning analytics approach. Significant positive correlations were found for the learning behavior of using markers with learning performance and CPS awareness in group discussion, while significant negative correlations were found for some factors of STEM learning strategy and learning behaviors in pre-learning with some factors of CPS awareness. The results imply the importance of an efficient approach to using learning strategies and functional tools in STEM education

Seven HCI Grand Challenges

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; Well-being, Health and Eudaimonia; Accessibility and Universal Access; Learning and Creativity; and Social Organization and Democracy. Although not exhaustive, they summarize the views and research priorities of an international interdisciplinary group of experts, reflecting different scientific perspectives, methodological approaches and application domains. Each identified Grand Challenge is analyzed in terms of: concept and problem definition; main research issues involved and state of the art; and associated emerging requirements

Real-Time Learning Analytics System for Improvement of On-Site Lectures

[Purpose]The purpose of this study is to propose a real-time lecture supporting system. The target of this study is on-site classrooms where teachers give lectures and a lot of students listen to teachers’ explanations, conduct exercises, etc. [Design/methodology/approach]The proposed system uses an e-learning system and an e-book system to collect teaching and learning activities from a teacher and students in real time. The collected data are immediately analyzed to provide feedback to the teacher just before the lecture starts and during the lecture. For example, the teacher can check which pages were well previewed and which pages were not previewed by students using the preview achievement graph. During the lecture, real-time analytics graphs are shown on the teacher’s PC. The teacher can easily grasp students’ status and whether or not students are following the teacher’s explanation. [Findings]Through the case study, the authors first confirmed the effectiveness of each tool developed in this study. Then, the authors conducted a large-scale experiment using a real-time analytics graph and investigated whether the proposed system could improve the teaching and learning in on-site classrooms. The results indicated that teachers could adjust the speed of their lecture based on the real-time feedback system, which also resulted in encouraging students to put bookmarks and highlights on keywords and sentences. [Originality/value]Real-time learning analytics enables teachers and students to enhance their teaching and learning during lectures. Teachers should start considering this new strategy to improve their lectures immediately

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment

Robotic Stand-up Comedy: State-of-the-Art

Humanoid and social robots have to perform in socially acceptable ways. They interact with humans and support humans in their needs and their activities. Stand-up comedy is an extreme form of human-human and human audience interaction. It can be mild, but often it goes beyond what is socially accepted in verbal and nonverbal behavior and expressed opinions. But it makes people laugh and we can ask whether this can be done by robots and what we can learn from it for other ways of robot-human or robot audience interaction. In this paper we confine ourselves to a survey of developments in robotic stand-up comedy. We hope that this survey helps to stimulate research in this area and identify topics of more general interest in robot-human interaction

Ubiquitous computing in the real world: lessons learnt from large scale RFID deployments

Ubiquitous computing technologies are slowly finding their way into commercial information systems, which are often constructed at considerably larger scale compared to what is possible in research demonstrators. Furthermore, lengthy and costly preparation or upgrade of existing infrastructures, training of employees and users in the new ways of working, controlled introduction of new functionality, features and services to manage risk, unexpected behaviors due to the wider variety of possible real-world situations, incremental approach to systems development so as to better identify successful aspects, regard for the economics of systems as a core requirement, and selection of open or closed systems are all issues that are mostly outside the scope of current ubiquitous computing research but play a critical role in industrial deployments. In this paper we review two case studies of fully operational Radio Frequency Identification-based systems: the Oyster card ticketing system used at the London Underground in the UK, and retail applications deployed at the Mitsukoshi departmental stores in Tokyo, Japan. We examine each case in terms of technologies, user interactions, and their business and organizational context and make several observations in each case. We conclude by drawing general lessons related to ubiquitous computing in the real world and identify challenges for future ubiquitous computing research

Measuring Behaviors and Identifying Indicators of Self-Regulation in Computer-Assisted Language Learning Courses

The aim of this research is to measure self-regulated behavior and identify significant behavioral indicators in computer-assisted language learning courses. The behavioral measures were based on log data from 2454 freshman university students from Art and Science departments for 1 year. These measures reflected the degree of self-regulation, including anti-procrastination, irregularity of study interval, and pacing. Clustering analysis was conducted to identify typical patterns of learning pace, and hierarchical regression analysis was performed to examine significant behavioral indicators in the online course. The results of learning pace clustering analysis revealed that the final course point average in different clusters increased with the number of completed quizzes, and students who had procrastination behavior were more likely to achieve lower final course points. Furthermore, the number of completed quizzes and study interval irregularity were strong predictors of course performance in the regression model. It clearly indicated the importance of self-regulation skill, in particular completion of assigned tasks and regular learning

Relation Analysis between Learning Activities on Digital Learning System and Seating Area in Classrooms

Eleventh International Conference on Educational Data Mining (EDM 2018) : July 15-18, 2018, Buffalo, NY USAThis paper discusses a relation analytics between learning activities and seating area in classrooms. Learning activities are collected via digital learning systems; including a learning management system, an e-portfolio system and an e-Book system. The activities are converted into barometers which indicate the amount of activities such as quiz scores, report scores, action frequencies on e-Books, length of journals, etc. The classroom is divided into 12 subareas, and the correspondence between students and the areas are also collected via the learning management system. We applied classical statistical analyses to the collected data. Through the experiments with about 200 students over 14 weeks, we found out that the seating area has strong relationship to learning activities