Practice and Analysis of Asynchronous Distance Information Literacy Lectures Based on Blended Lecture Materials

  • Shin'nosuke Yamaguchi Kyushu Institute of Technology
  • Hideki Kondo Kanda University of International Studies
  • Yoshimasa Ohnishi Kyushu Institute of Technology
  • Kazunori Nishino Taisei Gakuin University
Keywords: Learning analytics, Blended lecture, Asynchronous distance lecture, e-Learning, Learning management system

Abstract

In this paper, we report on a case study in which we converted blended information literacy lectures into asynchronous distance lectures using the basic features of a learning management system. In the implementation of asynchronous distance lectures, it is necessary to maintain the learning activity of students and to achieve the same learning effect as in blended lectures. Preparing materials for asynchronous distance lectures from scratch is a heavy burden on the teacher. Therefore, we change the style and materials of the lectures based on the learning analysis of blended lectures that we have already practiced. We update the material, add quizzes with deadlines, and report on the results of asynchronous distance information literacy lectures. We then analyze and evaluate student learning to determine if this has been effective.

References

Yasushi Fuwa, Hisayoshi Kunimune, Katsumi Wasaki, Masaaki Niimura, Yasunari Shidama, Yatsuka Nakamura, “The Current Activities and the Future Plan of Graduate School of Sci-ence and Technology on the Internet, Shinshu University”, Information Management, 47(8), pp.547-553, 2004

Osamu Yamakawa, “Possibility of Learning Analytics beyond the Borders of Organizations‒A Critical Review on the Possibility”, Computer & Education Vol38, pp56-68, 2015.

Takeshi Matsuda, Masanori Yamada, Yoshiko Goda, Hiroshi Kato, Hiroyuki Miyagawa, “Development of ‘Self-Regulator’ that Promotes Learners to Establish Planning Habit and its Formative Evaluation”, Japan Journal of Educational Technology, No 40, pp137-140, 2017.

New Media Consortium: NMC Horizon Report 2013 Higher Education Edition (2013).

Shin-ichiro Kubota, Naonobu Okazaki, “Consideration for Detecting Variation of Learners’ Behavior based on a Learning Log of LMS”, SIG Technical Reports, Vol.2017-CLE-22 No.3, p6 (2017).

Akiko Ishikawa, Kayo Ogawa, Piotoyo Hartono, “Discovering Students Characteristics Us-ing Learning History Data”, Journal of Information and Systems in Education, Vol 31, No 2, pp185-196, (2014).

Cristobal Romero, Manuel-Ignacio Lopez, Jose-Maria Luna, Sebastian Ventura, “Predicting students’ final performance from participation in on-line discussion forums”, Computers & Education Vol 68, pp468-472 (2013).

Christothea Herodotou, Bart Rienties, Martin Hlosta, Avinash Boroowa, Chrysoula Mangafa, Zdenek Zdrahal, “The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study”, The Internet and Higher Edu-cation 45, 100725, 2020.

Yoshiko Goda, Masanori Yamada, Hiroshi Kato, et al, “Procrastination and other learning behavioral types in elearning and their relationship with learning outcomes”, Learning and Individual Differences Vol. 37, pp.72-80 (2015).

Ruriko Taniguchi, “Comparisons of Web-based Learning Assistance Methods Using Usage Rates and Scores”, Vol25, No.3 pp.321-328 (2008).

Thomas Lerche, Ewald Kiel, “Predicting student achievement in learning management systems by log data analysis”, Computers in Human Behavior Vol 89, pp367-372 (2018).

Shin’nosuke Yamaguchi, Hideki Kondo, Yoshimasa Ohnishi, Kazunori Nishino: “Analysis of Student Activities in Blended Information Literacy Lectures”,9th International Congress on Advanced Applied Informatics (IIAI-AAI), pp228-233, 2020.

Shin’nosuke Yamaguchi, Hideki Kondo, Yoshimasa Ohnishi, Kazunori Nishino, “Analysis of Learning Activities and Effects on Blended Lectures”, Procedia Computer Science Vol 159, pp.1568-1575 2019.

Moodle, https://moodle.org/ (Retrieved March 20, 2021)

Published
2023-04-13
Section
Technical Papers