There is an urgent need to develop human resources which can utilize the power of AI and data in order to enrich society through technological innovation and value creation. On the other hand, in school education, due to the framework of each subject and time limitation, it is difficult to implement a curriculum to work on application creation. Therefore, in this study, we attempted to develop and evaluate teaching materials for developing iOS applications for junior high and high school students in order to provide opportunities for interested students to learn independently during after-school hours. As a result, the students’ responses on programming showed no significant change for the experienced students, but “surprise” and “feeling that I can do it” were meaningful learning opportunities for the beginners. Looking at the responses of the students regarding AI, although they knew the term AI, their perceptions varied from “humanistic”, “familiar”, and “coexistence with AI”.
This paper is based on a study that developed and used a predictive evaluation method to evaluate educational software with embedded Artificial Intelligence (AI) functionalities for children’s learning of English pronunciation. The approach built on Squires and Preece’s [1] heuristics which used a social constructivist view of learning but were developed before AI functionalities were commonplace in educational software. Three AI-powered English learning apps were selected for the predictive evaluation. The evaluation enabled a comparison between the AI-powered English-learning apps based on their potential for learning. The study is the first to predictively evaluate pedagogical values of AI functionalities for improving ESL pronunciation and may provide an approach to developing predictive evaluation more widely for educational software that embeds AI functionalities.
In recent years, Japan’s education system has started regarding trial-and-error favorably and is accordingly evaluating its strategy. Evaluation of trial-and-error has become crucial to learning analytics. Jigsaw Code is a programming exercise that gives students shuffled code blocks of a solution program, including fake ones, and asks them to rearrange the blocks in the correct order. It logs the solving actions of each student. This paper shows that the cycles in the state transition of the solution indicate the students’ trial-and-error process. Two hundred and thirty undergraduates solved 11 puzzles (problems) of Jigsaw Code. We analyzed the transition of the solution sequence at each solving action and found cycles in the state transition. For 97
Through a lexical analysis, this study examines the relationship between the terms used in various educational resources about robots. These resources were authored by novice or expert teachers in primary schools. Our hypothesis is that the computer science concepts discussed in an activity are different depending on the type of robot. The first results confirm dependence between the type of robot and the lexicon used in the resources. The corpus is explored according to three thematic sets of terms: Computer Science (CS), Pedagogy and Move with special attention for CS terms to compare the vocabulary used for sequential and event-driven programming robots.
In this contribution the resilience of students in the computer and information literacy (CIL) domain is focused. In this context, research towards individual student resilience is of relevance in order to examine characteristics from the student level that can be used as setscrews by educators and other educational stakeholders to minimize or overcome social issues in the CIL domain. Taking advantage of the representative cross-sections of students from the International Computer and Information Literacy Study 2018 (ICILS 2018), the question of the prevalence of resilient students (research question 1), differences between educational systems in international comparison (research question 2), and students’ related antecedents and process factors from the ICILS 2018 contextual model (research question 3) have been focused via using a logistic regression approach. The sample consisted of 46,561 students aged 14 from 14 countries. Cross country analyses revealed that student’s sex and their cultural capital are the strongest predictors for resilience in the CIL domain. However, including family’s process characteristics shows that students’ self-efficacy toward the use of information and communication technology (ICT), their use of ICT for information-related activities itself and the use of ICT for basic and advanced purposes have been identified as significantly related to student resilience.
