Although “school-to-work transition” has recently attracted considerable attention, most studies on this topic have conducted retrospective surveys. Some existing studies have recently focused on students’ experiences at university. However, the studies do not provide perspectives on effect of students’ experiences on proactive behavior. Therefore, this study focuses on proactive behavior at work that could be linked to outcomes of active learning at universities. Using surveys conducted over three years, form 2010 to 2013, and covariance structure analysis, this study investigates the effects of undergraduates’ learning behavior and student-life fulfillment on proactive behavior after joining their work organization. The data revealed that students who belong to an out-of-class community and who have a sense of fulfillment in their student life display a positive effect on their proactive behavior.

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This study aims to describe a teacher’s practical knowledge by applying conversation analysis. Twenty hours of video recordings of English lessons at a public junior high school in the Kansai area were analyzed with a focus on the critical incident. The results showed that when a critical incident happened in the IRF sequence: (a) the teacher examined the student’s understanding using several formulation practices based on the student’s reaction, and (b) using other students as an affordance, the teacher implicitly showed the student how to respond properly, and finally, elicited the correct response from the student. The teacher’s practical knowledge is described as mutual acts between the teacher and students. In conclusion, the author discusses the feasibility of using conversation analysis to describe a teacher’s practical knowledge, and suggests how conversation analysis can contribute to future research.

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This paper describes a design and practice of a student experiment on “artificial intelligence (AI).” AI has a wide range of topics that are independent of each other so that it is difficult for students to understand relationships between the topics. To improve this problem we designed our AI experiment based on Minsky's Mental Activity Model, which has six layers each of which expresses a level of human mental activity. By associating each week's topic of the experiment to the layer, students can easily grasp the relationship between the topics. In the student experiment, students program a mobile agent both with an actual equipment (e-puck) and in the simulator environment. The student experiment conducted for three years and we did a questionnaire each year. The result shows most of the students understood an association between the weeks' topics and Minsky's model layers, which also indicates they grasped the relationship between the AI topics.

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We explored the relationships between critical thinking attitude, reflective predisposition, perceived academic competence, and the educational program. A two-wave questionnaire survey with a four-month interval was administered to 312 elementary school students (5th and 6th graders) and 306 junior high school students (7th–9th graders) in Japan using two scales: to measure critical thinking attitude in general (CT-G, a 10-item scale) and in study (CT-S, a 10-item scale) . In addition, 125 students (the same 7th graders) completed the scales 28 months later. Results indicated satisfactory internal consistency reliability and construct validity of both scales. The CT-G and CT-S scales were significantly correlated with the cognitive reflective-impulsive predisposition and perceived academic competence, supporting their criterion-related validity. A simultaneous path analysis revealed that during each wave, (i) reflective predisposition influenced CT-G, (ii) CT-G affected CT-S, and (iii) CT-S affected perceived academic competence. Finally, the effects of educational program are discussed.

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Academic achievement of students has been analyzed based on progresses of their semester GPAs, combined with results of placement tests, learning history in high-schools and results of the entrance examination. It turns out that students, having GPAs below 1.5 in the first year spring semester, tend to drop out. More than 50% of these students can be classified by the placement test for mathematics. In contrast, the semester GPA are less correlative to the learning history and entrance examination. On the basis of these results, we discuss the possibility of assessment for the future of student.

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