The purpose of this study is to explore the factors that have the most decisive influence on actual learning intention that leads to participation in adult education. For developing the predictive model, we used tree-based machine learning, with the longitudinal big data (2017~2020) of Korean adults. Based on the gradient boosting machine (GBM) results, among the eleven variables used, the most influential variables in predicting the possibility of lifelong education participation were self-pay education expenses and then highest level of education completed. After the grid search, not only the importance of the two variables but also the overall figures including the false positive rate improved. In future studies, it will be possible to improve the performance of the machine learning model by adjusting the hyper-parameters that can be directly set by less computational methods.

This study aims to identify factors to be considered when developing Information and Communication Technology (ICT)-integrated classroom models and to suggest a conceptual framework for considering more appropriate classroom models, tailored to the environments and needs of each developing country. In-depth interviews were conducted with experts, yielding many factors to be taken into account when integrating ICT in various educational contexts of developing countries. The factors are categorized into five domains: current status and relevant policies in ICT education, ODA (Official Development Assistance) goals and priority setting, infrastructure and technical challenges, pedagogical methods, and educational resources. Based on the recommendations elicited for the five categories, we suggest a conceptual framework that provides practical guidance on the criteria for selecting classroom models suited to each partner country’s environments and contexts. This study also draws implications from the interviews for classroom design guidelines related to hardware and infrastructure, to improve the sustainability of ICT-integrated classroom projects.

As innovation barriers and inertia work against the application of new technologies in educational settings, reducing them will help overcome innovation resistance and increase the acceptance and adoption of this technology. This study aims to examine what types of innovation barriers and inertia there are (as perceived by users), how the types of users who perceive innovation resistance could be divided, and which characteristics differ depending on the type of user in a situation where a university adopts a new LMS as a learning technology tool for innovation. This study derived risk barriers, usage/value/tradition barriers, image barriers, and inertia as four factors that affect innovation resistance. The results of the study suggest that new LMS-related personalized support and training programs should be developed according to the specific needs and characteristics of each user cluster.

This study examines how students’ situational interest changes across different segments of peer reading activities and identifies the relationship with personal traits like the students reading skills and motivation. The authors measured their levels of situational interest, reading skills, and reading motivation, based on their participation in individual and peer reading using PALS. The result revealed that situational interest was rather stable across different segments during peer reading activities. However, the change in situational interest differed between less competent readers and more competent readers across different reading tasks. The less proficient readers showed a significant increase in situational interest before and after peer reading. Furthermore, situational interest during peer reading activities did not correlate with reading motivations, contrary to the result of the individual reading task. The findings suggest that peer reading may be more beneficial than individual reading when teaching learners with low reading skills or motivation.

KSII Transactions on Internet and Information Systems TIIS Paper Details