Personalized Distance Learning Research Articles

Personalized distance-learning experience in determining students’ performance

One way or another, students face certain difficulties and disadvantages of distance learning. This can affect students physically and emotionally if appropriate measures are not taken. The solution might be the personalization of distance learning process, which implies the application of individual approaches to learning. The article aims to determine the academic performance of students who use the system of personalized distance learning Quiz Guide. For this study, 140 students were selected. The results of the performance analysis showed that the experimental group scored higher on the final test, indicating that students using the Quiz Guide learning system had significantly better academic achievement than those who took the traditional approach to learning. According to the post-test results, the control group scored (average) 65.14 points, the experimental group, 74.71. Keywords: Academic performance, computer learning system, individualized approach, personalized distance learning, Quiz Guide system.

Особенности восприятия дистанционного обучения школьниками и студентами

The article presents arguments for necessity to determine the attitude of education actors to a new distant form of learning. The results of the study of semantic assessment of distant learning by students and schoolchildren have been presented. The sematic differential has been used to analyze the data obtained. The semantics of distant learning identified by its actors on the basis of the categories of “comfort”, “intellectual efficiency”, “workload”, and “externality”. There are differences in the semantic assessment of distant learning by students and schoolchildren. Students gave much lower estimates to distance learning by intellectual efficiency parameter, while estimating the workload parameter higher. Schoolgirls gave negative assessment to distance learning by all parameters. The findings indicate that there is a need to arrange personalized distance learning.

Development of virtual radiology clinical competency evaluation amidst COVID-19 at a dental school.

To describe and report on the use of virtual clinical competency examinations (VCCEs) for D3 dental students in a radiology clinic during the COVID-19 pandemic and relate the technology to other clinical and educational applications. Paper-based clinical competency examinations (CCEs) were converted to three different VCCEs to closely replicate the CCEs. Existing grading rubrics were used to assess students' abilities to evaluate technical issues and interpret intraoral full-mouth series and panoramic radiographs. The grading rubrics were modified from their clinical versions to eliminate non-reproducible skills, such as patient management and application of selection criteria. Radiographs were selected from clinic records and anonymized. VCCEs were conducted via WebEx conferences that were scheduled via an appointment app. These experiences can be extended to virtual patient contacts, teleradiology, and telemedicine, as well as continuing dental education experiences. Approximately 200 VCCEs were administered by four oral and maxillofacial radiology faculty over a 10-week period. Both students and faculty managed the technology well, with few missed appointments. All students were successful in challenging the examinations and appeared to be satisfied with the experience. Although direct comparisons between the CCEs and the new VCCEs are difficult, as there was no time to pilot the VCCEs, students were successful in challenging the examinations and informally reported satisfaction with the experience. Although components of CCEs that could not be replicated on the VCEs could not be assessed, it is felt that the students had adequate experience in selection of radiographs and patient management prior to the COVID-19 lockdown. Additional benefits of the VCCEs included contact with students to check on their well-being. The experience indicates that VCCEs can be used in the future in hybrid models of oral & maxillofacial radiology education, clinical care, patient screening, and telemedicine. Moreover, the success of this demonstration points to potential for personalized distance learning in continuing dental education.

No Anxious Student Is Left Behind: Statistics Anxiety, Personality Traits, and Academic Dishonesty—Lessons from COVID-19

Scholarly studies have revealed that exposure to statistics courses affect students’ anxiety levels and that this has been associated with unethical misconduct. Thus, the present research’s main objective is to comprehend the mediating role Statistics Anxiety plays on the relationship comprising students’ personality traits and academic dishonesty as manifesting before and during the COVID-19 pandemic. Its aim is to understand this phenomenon and provide theoretical tools for fostering sustainably personalized distance learning and instruction. Data were collected from students studying for a bachelor’s degree in the social sciences at three different Israeli colleges. The sample comprises 316 participants and data were analyzed using Structural Equation Modelling (SEM). The results show a significant mediation manifested by an indirect effect between personality traits and academic dishonesty via statistics anxiety only in emergency remote teaching, although no parallel significant mediation was observed in the face-to-face course. These results could be explained by differences in delivery methods. Thus, we recommend that in the emergency remote teaching environment instructors’ presence include: (1) supportive, emphatic interaction to reduce virtual distance and (2) Social Emotional Learning (SEL) strategies, which foster students’ learning challenges and prevent anxiety and academic dishonesty.

Personalized Distance Learning System based on Sequence Analysis Algorithm

Personalized learning system can provide users with the most valuable learning resource to them through intelligent recommendation models and algorithms. This paper proposed the classical sequence analysis algorithms, and the Prefixspan algorithm is validated through distance learning platform data. In the event that the minimum support threshold is between 0.003 to 0.004%, test data shows that the performance of the algorithm's accuracy rate is relatively stable and the recommendation effect is satisfactory.

Research of personalized Web-based intelligent collaborative learning

At present, the network collaborative learning in education has been widely used, but they have some problems were not resolved, which like lacks intelligence or can not accurately master information requirements. In the research, using intelligent agent technology in the field of distributed artificial intelligence, to propose a model in collaborative learning process. The model based on the learner's cognitive level, learning ability, cognitive ability, can adjust teaching methods, teaching strategies, achieve individualized to help the user personalized distance learning. Introduce intelligent agent technology, to construct a master program, student agent, teaching agent and collaborative learning model of intelligent agents.

A Distance Learning System for Delivering Appropriate Studying Materials and Stimulating Learner Volition

In this paper, we propose an agent-based personalized distance learning system for delivering appropriate studying materials to learners by judging learners’ degree of understanding.The main elements of our proposed system are the agents, which play the role of teacher. Based on the learning history they analyze the understanding degree of learners. To evaluate the proposed distance learning system, we carried out three experiments and a questionnaire investigation. The evaluation results show that by making groupings of learners, the agents can decide what kind of materials should be given to learners. Furthermore, we improved the system by adding new features such as mental action of color and the competition with other learners. The evaluation results show that an improved system can increase learning efficiency.