Pi-Shan Hsu's research while affiliated with Fooyin University and other places

Based on the self-determination theory, we can promote the learner self- determination ability to affect their learning achievement. This research is different than the traditional on-line platform designed; we used self- determination theory as a learning approach, and design the online platform for students’ self determination. According that, the online-self determination platform has the characteristics, such as: having a learning path for self-determination, strengthening the sense of independence, providing a diagram for cognitive loading theory (CLT), promoting abilities of self-assessment, diagnosing learning situation, using online discussion and interaction to construct a social group.

The purpose of the study was to explore that using creative problem solving can promote students’ performance of concept mapping (CMPING). Students were encouraged to use creative problem solving (CPS) in constructing nanometer knowledge structure, and then to promote the performance of CMPING. The knowledge structure was visualized through CMPING which helps students to improve their performance. The participants were 42 college juniors who selected the course “Nano-environmental Engineering Technology”. Four instruments were used to classify student learning performance (meaningful learning, rote learning and non-learning). This study included three main issues: (1) Student learning quality was determined by the change in concept map construction. (2) In-depth interviews were applied to understand student’s CPS process. (3) Student interaction quality in a discussion board on a web-platform was evaluated. The results showed that meaningful high-level learners successfully applied CPS in constructing concept maps and they presented better performance of CMPING. Rote learners’ results were in the second place, and non-learners achieved the worse outcomes. It is suggested that a future teaching study can use creative problem solving to promote students’ performance of CMPING in other courses.

The objective of this research is to validate the algorithm of learning effort which is an indicator of a new real-time and non-interfering based diagnostic technique. IC3 Mentor, the adaptive e-learning platform fulfilling the requirements of intelligent tutor system, was applied to 165 university students. The learning records of the subjects who attended IC3 Mentor were converted into Characteristic Learning Effort (CLE) curves through the algorithms of learning effort. By evaluating CLE curves and questionnaire survey reports, the findings indicate that the learning effort algorithm is verified to be an effective real-time and non-interfering diagnostic technique. Furthermore, CLE curve is proven to be an effective user-friendly tool for learners and instructors in diagnosing learning progress under adaptive e-learning context. The CLE curve generated by the algorithm of learning effort is a visualized graphic tool which can be applied in the adaptive e-learning platform of education and industry fields.

The purpose of this research is to establish the indicators of self-determined learning platform. The research has developed the indicators with three major concepts, and 41 indicators. The finding of the research shows of the research shows of the research shows definition of SDL, we emphasizing self-determination according to the degree that people have different motives in different types of regulation, in which basic needs into three a psychological need. Based on literature review, this study assume that SDL through the social context of interaction with peers and teachers. This study also applied the concept of SDL into online platforms and plans to detect student's e-decision thinking, and e-decision-making by SDL platform.

A dynamic real-time algorithm of Learning Progress Motivation (LPM) is validated for dynamically diagnosing engineers’ learning progress and innovation performance at a real-time base in innovation processes. One hundred and three engineers participate in the situated experiments which simulate innovation contexts are motivated by LPM. Subjects’ learning progress and innovation performance are converted into quantitative data by LPM algorithm and then represented by a LPM characteristic curve. Through analyzing the LPM characteristic curve and subjects’ process-phase records from experiments, the findings show that LPM facilitates continuous learning and innovation through four-phase cycles and the LPM characteristic curve tends to converge toward a steady-state condition in which innovation deactivation takes place. Furthermore, the navigation effect of LPM algorithm is discovered and which enhances subjects’ continuous learning and innovation. The LPM Characteristic curve is proved to be a user-friendly visualized tool for diagnosing the status of learning progress and innovation performance in innovation processes.

Learning Progress Motivation (LPM) was developed to intrinsically incent organizational members adapting both exploratory and exploitative learning in innovation processes. LPM was applied on 103 engineers in the experiments which simulated innovation contexts. The computational technique of LPM was developed to dynamically transform learning records to a Learning Progress Characteristic (LPC) curve at real-time base. Through analyzing LPC curve, LPM four-phase cycles and the steady-state condition of LPM innovation process were identified. Engineers are motivated intrinsically and continuously by LPM to pursue maximal learning progress. The learning progress, innovation performance, and steady-state condition can be diagnosed by interpreting LPC curve. Therefore, the visual aided LPC curve is a user-friendly tool for managerial leaders of organizations in diagnosing learning and innovation status in innovation processes. KeywordsDiagnosis–innovation–intrinsic motivation–learning progress

The level of learners’ expertise has been used as a metric and diagnostic mechanism of instruction. This metric influences mental effort directly according to the applications of cognitive load theory. Cognitive efficiency, an optimal measurement technique of expertise, was developed by Kalyuga and Sweller to replace instructional efficiency in e-learning environment for dynamically adapting instruction. But mental effort, a factor of cognitive efficiency, is assessed by a node mode measurement technique which discontinues instruction and causes interference. This research proposes a new adaptive e-learning measurement technique which assesses learning effort in a dynamic, real-time, and non-interference instructional process. The learning effort curve is a key diagnostic to enhance interaction between instructors and learners in an adaptive e-learning instructional process. Purchase this article to continue reading all 7 pages >

The objective of this research is to validate the algorithm of learning effort which is an indicator of a new real-time and non-interfering based diagnostic technique. IC3 Mentor, the adaptive e-learning platform fulfilling the requirements of intelligent tutor system, was applied to 165 university students. The learning records of the subjects who attended IC3 Mentor were converted into Characteristic Learning Effort (CLE) curves through the algorithms of learning effort. By evaluating CLE curves and questionnaire survey reports, the findings indicate that the learning effort algorithm is verified to be an effective real-time and non-interfering diagnostic technique. Furthermore, CLE curve is proven to be an effective user-friendly tool for learners and instructors in diagnosing learning progress under adaptive e-learning context. The CLE curve generated by the algorithm of learning effort is a visualized graphic tool which can be applied in the adaptive e-learning platform of education and industry fields.