Enhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems

Wanichsan, Dechawut; Panjaburee, Patcharin; Chookaew, Sasithorn

doi:10.1016/j.caeai.2021.100013

Cited by 14 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A. The Self-Regulated Personalized Online Learning System (SPOLS) SPOLS, the developing system, originated from previous scholars [11][12][13][14][15][16][17][18][19][20][21][22][23]. These previous studies relied on a concepteffect-oriented and preference-based learning system that aims at personalized instructional material, respecting preferences such as learning status, achievement, and the time used.…”

Section: Design and Developmentmentioning

confidence: 99%

Design and Assessment of a Personalized Online Mathematics Learning System with Self-Regulated Learning Features: An Educational Design Research

Panjaburee

2024

IJIET

View full text Add to dashboard Cite

A personalized online learning system incorporating a self-regulated learning approach was developed to deliver the factorization in quadratic polynomials topic on mathematics learning. This system created four learning materials for learning a certain topic in mathematics, corresponding to the students’ characteristics. This study followed the education design research approach to develop a self-regulated-based personalized online learning system and evaluate the system from a technology acceptance perspective. The Pearson correlation was computed and revealed the best fit of the collected data for further stepwise multiple regression analysis through students’ acceptance of learning mathematics before and after using the developed system. Furthermore, the Chi-square test was performed to determine the acceptance change and frequency rated by items to ensure an in-depth understanding of how acceptance changed before and after. Although the findings revealed that students’ perceived ease of use was primarily a predictor of their attitude about the system, they showed an increment in perceived usefulness after participating in the developed system. The students’ perceptions of ease and attitude toward the self-regulated-based personalized online learning system for mathematics learning should be mainly considered in further development, including the different learning approaches for supporting mathematics learning.

show abstract

Section: Design and Developmentmentioning

confidence: 99%

Design and Assessment of a Personalized Online Mathematics Learning System with Self-Regulated Learning Features: An Educational Design Research

Panjaburee

2024

IJIET

View full text Add to dashboard Cite

show abstract

“…In this study, experts will give weight to each test item based on the closeness of the test item's relationship with the existing concept for building CER [11]. The CER weighting process is still to be further developed by other researchers [12] [22], offering a new procedure in which the integration of the opinion of the relationship of concept-test items based on the majority density of several experts to enhance the quality of the CER is proposed and used as personalized feedback. This technique is practical for reducing inconsistencies in the weighting criteria of multiple experts and enhances the overall learning diagnostic procedure for developing learning path systems.…”

Section: Related Workmentioning

confidence: 99%

“…With the CER model, students' learning difficulties can be identified effectively [11]. Additionally, the CER model can be applied to provide personalized feedback to students and help teachers identify misconceptions [12]. Consequently, the utilization of the CER model in establishing learning paths gained significant research [2] [13].…”

Section: Introductionmentioning

confidence: 99%

Concept–Effect Relationship Weighting Based on Frequency of Concept’s Co-Occurrence for Developing Personalized Remedial Learning Path

Wahyuningsih,

Djunaidy,

Siahaan

2024

IEEE Access

View full text Add to dashboard Cite

Notably, a personalized learning path is advantageous for students who need remedial assistance due to their struggle to master concepts and poor exam performance. Personalized learning paths produced from conceptual maps have proven highly effective. Nevertheless, in establishing personalized learning paths using conceptual maps, the manual assignment of weights that depict the degree of relationship between test items and concepts is time-consuming and ineffective. To overcome the drawbacks of manually assigning weights, we propose an auto-generated concept relationship weighting method based on the co-occurrence between primary concepts and supporting concepts of test items in this study. Moreover, we build a system that generates a personalized remedial learning path for students using the proposed weighting method and a conceptual map. To evaluate the effectiveness of the proposed approach, we carried out an experiment on high school students. As shown by the experimental results, the generated personalized remedial learning path can significantly enhance the learning achievement of underachieving students. Furthermore, the new auto-generated weighting method was able to eliminate the time taken to assign weights by experts and resolve conflicting weight values.

show abstract

“…AIE can involve intelligent tutoring systems, virtual reality simulations, adaptive learning platforms, automated grading systems, and data analytics [6][7][8][9]. AIE technologies can help personalize learning, provide intelligent feedback, support student engagement, and optimize curriculum design [10][11]. In addition, AI education focuses on educating individuals about AI.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Teachers’ AI Competencies through Artificial Intelligence of Things Professional Development Training

Kitcharoen,

Howimanporn,

Chookaew

2024

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

The rapid increase in new challenges of the combination of the Internet of Things (IoT) and artificial intelligence (AI), which are emerging technologies, can play a compelling role in prompting the development of artificial intelligence Internet of Things (AIoT). Therefore, the demand for AI competencies for everyone will increase. Educational institutes focus on encouraging AI education because the demand for AI-literate workers will increase in the industrial sector. However, teachers’ lack of AI knowledge is a significant barrier to AI education. Thus, developing the teacher’s AI competencies and educating them about how to use and teach students is critical. In this study, we proposed artificial intelligence of things professional development (AIoT-PD) training to prepare the AI competencies of teachers ready to teach. A quasi-experimental design with a two-day training workshop was conducted among 13 teachers to examine its impact on AI competencies, including AI knowledge, AI skill, and AI attitude. The quantitative data were collected via a pretest and posttest after the training activity, while qualitative data were collected via interviews. This study showed that teachers’ AI knowledge significantly improved. These findings revealed the AIoT training workshop’s effectiveness in enhancing teachers’ AI competencies, which can help them effectively teach students in AI education.

show abstract

Enhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems

Cited by 14 publications

References 26 publications

Design and Assessment of a Personalized Online Mathematics Learning System with Self-Regulated Learning Features: An Educational Design Research

Design and Assessment of a Personalized Online Mathematics Learning System with Self-Regulated Learning Features: An Educational Design Research

Concept–Effect Relationship Weighting Based on Frequency of Concept’s Co-Occurrence for Developing Personalized Remedial Learning Path

Enhancing Teachers’ AI Competencies through Artificial Intelligence of Things Professional Development Training

Contact Info

Product

Resources

About