Beyond Learning Analytics: Framework for Technology-Enhanced Evidence-Based Education and Learning

26th International Conference on Computers in Education, Metro Manila, Philippines, November 26-30, 2018.Currently eLearning infrastructure across various institutions often includes aLearning Management System (LMS), various ubiquitous and classroom learning tools, Learning Record Stores (LRS) and Learning Analytics Dashboards (LAD). Such aninfrastructure can apply Learning Analytics (LA) methods to process log data and supportvarious stakeholders. Teachers can refine their instructional practices, learners can enhancelearning experiences and researchers can study the dynamics of the teaching-learningprocess with it. While LA platforms gathers and analyses the data, there is a lack of specificdesign framework to capture the technology-enhanced teaching-learning practices. Thisposition paper focuses the research agenda on evidence in a data-driven educationalscenario. We propose the Learning Evidence Analytics Framework (LEAF) and present theresearch challenges involved

ゴイガクシュウニテキセツナガゾウノスイセンニカンスルケンキュウ

博士（工学）東京農工大

Learner-Centric Technologies to Support Active Learning Activity Design in New Education Normal: Exploring the Disadvantageous Educational Contexts

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

Developing an early-warning system for spotting at-risk students by using eBook interaction logs

Abstract 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

Using Learning Analytics to Detect Off-Task Reading Behaviors in Class

[The 9th International Learning Analytics and Knowledge (LAK) Conference] March 4-8, 2019, Tempe, Arizona, USAIn this paper, we aimed at detecting off-task behaviors of the students by analyzing logs from a digital textbook reader. We analyzed 47 students’ reading logs from a 60-minutes long in-class reading activity. During the preprocess, we extracted each student’s reading patterns as a single vector. Then we used cluster analysis to find the most common reading patterns. Our results indicated that there are two major reading patterns in data. The first pattern is, the students who are following the instructor from the beginning until the end of the lecture. The second pattern is, students who are following the instructor’s pattern until the first 17th minute but not during the rest of the lecture. Based on these patterns we labeled first group as on-task students while the other group as off-task students. We also investigated academic performance of students in these two groups. Obtained results can be used to design data-driven support for in-class teaching. Instructors can plan interventions when off-task behaviors occur while the lecture is in progress

Supporting Teaching/Learning with Automatically Generated Quiz System

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Oct 15, 2018 in Las Vegas, NV, United StatesThis paper describes an automatic quiz generation system to support memory retention utilizing digital textbook logs as well as to reduce teachers' burden to create quizzes. An experimental was conducted to examine whether our automatically generated quiz system more effective in learners’ learning achievement than teacher-created quizzes. Twelve international students participated in the evaluation experiment. The result showed that our proposed system and teacher-created quizzes were equivalently effective. A correlation analysis was conducted in order to find the correlations among learning achievement, the number of quizzes and each variable in a questionnaire. It was found that the number of the quizzes and their learning achievement had strong positive correlation

Exploring Temporal Study Patterns in eBook-based Learning

28th International Conference on Computers in Education, 23-27 November 2020, Web conference.In this study, approximately 2 million click-stream data of 1346 students in the eBook platform were analyzed aiming to explore the temporal study patterns of the students followed during the lectures. The data used in the study collected from Kyushu University, Japan with the help of a digital textbook reader called BookRoll. Students used BookRoll for reading learning materials in and out of the class. To analyze the data we first, converted reading sessions into the sequence data which represents student’s weekly reading behavior, then we clustered students based on their study patterns. Our results revealed that three groups of students can be extracted with similar study patterns. Most of the students in Cluster 1 viewed the learning materials only during the class, without previewing and reviewing them. Students in Cluster 2 previewed the learning materials before the class, viewed learning materials during the class, and also reviewed after the class. Students in Cluster 3 viewed the learning materials during the class in the beginning but they became inactive over the period of time (week by week). Our study also showed how learning analytics can be used to understand students' study patterns which are difficult to do with self-report data. These results can help instructors while designing their courses

Learning Analytics to Share and Reuse Authentic Learning Experiences in a Seamless Learning Environment

Authentic learning experiences are considered to be a rich source for learning foreign vocabulary. Prevalent learning theories support the idea of learning from others’ authentic experiences. This study aims at developing a learning analytics solution to deliver the right authentic learning contents created by one learner to others in a seamless learning environment. Therefore, a conceptual framework is proposed to close the loops in the missing components of the current learning analytics framework. Data is captured and recorded centrally via a context-aware ubiquitous learning system which is a key component of a learning analytics framework. k-Nearest Neighbor (kNN) based profiling is used to measure the similarity of learners’ profiles. Authentic learning contents are shared and reused through re-logging function. This paper also discusses how two previously developed tools, namely learning log navigator and a three-layer architecture for mapping learners’ knowledge-level, are adapted to enhance the performance of the conceptual framework.[The 9th International Learning Analytics and Knowledge (LAK) Conference] March 4-8, 2019, Tempe, Arizona, US

A Platform for Image Recommendation in Foreign Word Learning

[The 9th International Learning Analytics and Knowledge (LAK) Conference] March 4-8, 2019, Tempe, Arizona, USAThis paper introduces a platform for image recommendation that can be used in informal learning of foreign words. The platform is based on a distributional semantics model (DSM) that is designed to recommend Feature-based Context-specific Appropriate Images (FCAIs) for representing a word. This technology is for a context-aware ubiquitous learning system that captures ubiquitous learning logs from various learning scenarios. This paper briefly discusses the data capturing tool, methods of employing learning analytics for ubiquitous learning logs analysis, natural language processing techniques applied for wordbank creation, and image embedding methods employed for feature analysis, development of an algorithm that determines the most appropriate FCAI images, and related scientific issues