Validation of Learning Effort Algorithm for Real-Time Non-Interfering Based Diagnostic Technique

2014 ◽  
pp. 206-219
Author(s):  
Pi-Shan Hsu ◽  
Te-Jeng Chang
Keyword(s):  
Real Time ◽  
Questionnaire Survey ◽  
Diagnostic Technique ◽  
Learning Context ◽  
Learning Platform ◽  
E Learning ◽  
Tutor System ◽  
Intelligent Tutor System ◽  
User Friendly ◽  
Intelligent Tutor

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.

Validation of Learning Effort Algorithm for Real-Time Non-Interfering Based Diagnostic Technique

2011 ◽  
Vol 9 (3) ◽  
pp. 31-44
Author(s):  
Pi-Shan Hsu ◽  
Te-Jeng Chang
Keyword(s):  
Real Time ◽  
Questionnaire Survey ◽  
Diagnostic Technique ◽  
Learning Context ◽  
Learning Platform ◽  
E Learning ◽  
Tutor System ◽  
Intelligent Tutor System ◽  
User Friendly ◽  
Intelligent Tutor

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.

A New Process Phase Diagnostic Technique

2011 ◽  
pp. 138-150 ◽  
Author(s):  
Pi-Shan Hsu ◽  
Te-Jeng Chang
Keyword(s):  
Real Time ◽  
Effective Interaction ◽  
Diagnostic Technique ◽  
Diagnostic Techniques ◽  
Learning Condition ◽  
Instructional Process ◽  
Learning Path ◽  
New Process ◽  
E Learning ◽  
User Friendly

By improving the imperfections of previous diagnostic techniques, the new process phase real-time diagnostic technique is developed to be suitable for an adaptive e-learning instructional process. This new diagnostic technique combines measures of a learner’s learning effort with associated performance in order to compare the efficiency of learning condition in a process phase, real-time, and non-interfering instructional process. The learning effort is represented as a visualized learning effort curve which is a user-friendly interface to enhance the decision making of learning path through the effective interaction between instructors and learners in an adaptive e-learning instructional process. The situated experiment was designed based on the new diagnostic technique and applied on 165 university students. In-depth group interview was conducted right after accomplishing the experiment. Results indicate that the learning effort curve is a capable real-time and non-interfering tool to diagnose learning progress in adaptive e-learning process.

EduTutor

2012 ◽  
pp. 49-63
Author(s):  
Joel J.P.C. Rodrigues ◽  
Pedro F. N. João ◽  
Binod Vaidya
Keyword(s):  
Virtual Environment ◽  
Primary Education ◽  
Learning Process ◽  
Education System ◽  
System Architecture ◽  
Study Guide ◽  
Large Set ◽  
Tutor System ◽  
Intelligent Tutor System ◽  
Intelligent Tutor

The system architecture and its main characteristics are described in detail. EduTutor focuses on subjects for the first cycle of studies of the Portuguese primary education system, between the first and the fourth year. Its purpose is to facilitate the perception of the learning process of each student, individually, in a virtual environment, and as a study guide. Furthermore, this intelligent tutor system was designed and its architecture was prepared for being easily integrated in higher levels of studies, different subjects, and different languages. EduTutor was validated with a large set of real cases and is being used, with success, in the Aulanet LMS platform.

scholarly journals Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

2020 ◽  
Vol 10 (2) ◽  
pp. 42
Author(s):  
Othmar Othmar Mwambe ◽  
Phan Xuan Tan ◽  
Eiji Kamioka
Keyword(s):  
Learning Styles ◽  
Real Time ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Accurate Information ◽  
Interactive Learning Environment ◽  
Time Dynamic ◽  
E Learning ◽  
Time Basis ◽  
User Friendly

Adaptive Educational Hypermedia Systems (AEHS) play a crucial role in supporting adaptive learning and immensely outperform learner-control based systems. AEHS’ page indexing and hyperspace rely mostly on navigation supports which provide the learners with a user-friendly interactive learning environment. Such AEHS features provide the systems with a unique ability to adapt learners’ preferences. However, obtaining timely and accurate information for their adaptive decision-making process is still a challenge due to the dynamic understanding of individual learner. This causes a spontaneous changing of learners’ learning styles that makes hard for system developers to integrate learning objects with learning styles on real-time basis. Thus, in previous research studies, multiple levels navigation supports have been applied to solve this problem. However, this approach destroys their learning motivation because of imposing time and work overload on learners. To address such a challenge, this study proposes a bioinformatics-based adaptive navigation support that was initiated by the alternation of learners’ motivation states on a real-time basis. EyeTracking sensor and adaptive time-locked Learning Objects (LOs) were used. Hence, learners’ pupil size dilation and reading and reaction time were used for the adaption process and evaluation. The results show that the proposed approach improved the AEHS adaptive process and increased learners’ performance up to 78%.

What to do and what to avoid on the use of gamified intelligent tutor system for low-income students

2021 ◽  
Author(s):  
Sivaldo Joaquim ◽  
Ig Ibert Bittencourt ◽  
Rafael de Amorim Silva ◽  
Patrícia Leone Espinheira ◽  
Marcelo Reis
Keyword(s):  
Low Income ◽  
Low Income Students ◽  
Tutor System ◽  
Intelligent Tutor System ◽  
Intelligent Tutor
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