Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

Abstract

Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system.

The Application Method of Big Data of Data Mining Algorithm in College Basketball Teaching

“…In the school's teaching, the use of data mining technology in basketball teaching can reasonably collect, analyze and summarize the relevant data of colleges and universities, and then find and solve problems in time, which not only reduces teaching accidents but also makes all aspects of colleges and universities. Management Metropolis eliminates drawbacks, coordinates the overall construction of the school, comprehensively cultivates high-quality talents and innovative ability levels, and promotes the construction of modern teaching in colleges and universities on the basis of improving the quality of teaching, laying a solid precondition for cultivating highquality talents, and for the realization of lay the foundation for the construction of world-class universities and first-class disciplines [18][19][20][21][22][23][24][25][26].…”

“…If there are problems in the process of education and teaching management, we must constantly summarize and summarize the emergence of such decision-making management problems, and at the same time identify them. Established in time, so that specific data can be turned into goals so that it can be redefined; the second method is to extract the original data, and the data can be managed and customized according to different goals, and because the teaching management information system e establishment of the database and other related teaching functions require the support of a lot of data [22]. In the process of extracting from the database, the noise data must be excluded, and it cannot be interfered with by the vacant data.…”

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Adaptive Knowledge Control in Digital Learning as a Factor in Improving the Quality of Education

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“…Russian scientists from Volgograd State Technical University have developed a system that is able to select a problem from the database of problems in accordance with the student's academic performance, solve it using programmatic reasoning, evaluate answers, determine the causes of errors, show explanatory feedback, ask clarifying questions and show elaborated examples [9]. The authors note that the evaluation of the system showed that its use led to moderate learning success, which was higher among students with low academic performance.…”