Abstract
With the advent of the Big Data era, information and data are growing in spurts, fueling the deep application of information technology in all levels of society. It is especially important to use data mining technology to study the industry trends behind the data and to explore the information value contained in the massive data. As teaching and learning in higher education continue to advance, student academic and administrative data are growing at a rapid pace. In this paper, we make full use of student academic data and campus behavior data to analyze the data inherent patterns and correlations and use these patterns rationally to provide guidance for teaching activities and teaching management, thus further improving the quality of teaching management. The establishment of a data‐mining‐technology‐based college repetition warning system can help student management departments to strengthen supervision, provide timely warning information for college teaching management as well as leaders and counselors’ decision‐making, and thus provide early help to students with repetition warnings. In this paper, we use the global search advantage of genetic algorithm to build a GABP hybrid prediction model to solve the local minimum problem of BP neural network algorithm. The data validation results show that Recall reaches 95% and F1 result is about 86%, and the accuracy of the algorithm prediction results is improved significantly. It can provide a solid data support basis for college administrators to predict retention. Finally, the problems in the application of the retention prediction model are analyzed and corresponding suggestions are given.
Highlights
Personalized learning refers to an educational process in which appropriate learning resources and learning methods are selected based on the learners’ cognitive level, learning ability, and their own qualities, so that they can make up for the shortcomings of their existing knowledge structures and achieve the best development [1,2,3,4,5]
As early as the Spring and Autumn period and the Warring States Period, Confucius, a famous educator in ancient times, proposed the idea of “teaching according to one’s ability and teaching without discrimination,” which was the beginning of the idea of personalized learning and is still highly respected even after more than two thousand years [6]
In 2016, the U.S Department of Education launched the National Education Technology Initiative (Future Ready Learning: Reimagining the Role of Technology in Education), and clearly defined “personalized learning refers to teaching that is optimized for each learner’s needs in terms of learning pace and teaching methods, and requires that the learning objectives, learning content and learning methods in the learning process should be different and adjustable according to the needs of learners” 11]
Summary
Personalized learning refers to an educational process in which appropriate learning resources and learning methods are selected based on the learners’ cognitive level, learning ability, and their own qualities, so that they can make up for the shortcomings of their existing knowledge structures and achieve the best development [1,2,3,4,5]. Complexity informatization continues to deepen, more and more Internet information technologies have flooded into the educational teaching process, which has led to a significant innovation in the development of education, and the emerging online learning model has gradually developed and become an integral part of educational learning [8]. Speaking, learning strategies are defined as those that can support and ensure learners to perform personalized learning recommendation strategies, focusing on online practice question recommendation algorithms, and so forth. is dissertation will be an exploratory study of the technical methods and applications to achieve personalized learning, using machine learning, data mining, and artificial intelligence technologies as technical tools, combined with knowledge from the field of pedagogy (learning theory, cognitive diagnosis theory, etc.)
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