대학생의 AI 기반 적응형 학습 플랫폼(ALEKS) 사용 만족도 및 학습성과에 대한 인식 연구

초록

Purpose: The purpose of this research is to analyze satisfaction and perception of learning outcomes regarding the use of AI-based adaptive learning platforms. Method: To achieve this, a survey was conducted among university students who took the business statistics course utilizing the AI-based adaptive learning platform(ALEKS), and data from 128 respondents were used in the final analysis. The frequency analysis, t-test, and F-test were conducted using IBM SPSS 22.0, and the subjective items were presented as word clouds and frequencies by type. Results: Firstly, the respondents were satisfied with the AI-based adaptive learning platform in the order of content, personalization, and user convenience. Secondly, in relation to class satisfaction associated with the use of the AI-based adaptive learning platform, learners rated the support from instructors (monitoring learning activities, providing feedback, etc.) and assistant support (platform guidance, problem-solving, etc.) lower than the average. Thirdly, concerning the perception of learning outcomes, the platform was most helpful in understanding their own level, but it was relatively lowly rated in terms of sparking interest, increasing learning motivation, and encouraging them to keep up with the learning progress without giving up. Fourthly, those who were more satisfied with the AI-based adaptive learning platform had statistically significantly higher class satisfaction and perceptions of learning outcomes than those who were less satisfied. Fifthly, the reasons for satisfaction with the classes utilizing the AI-based adaptive learning platform included ‘provision of diverse and sufficient problems’, ‘ability to learn independently after class’, ‘ability to personalize learning’, and ‘ability to confirm individual learning content and outcomes’. The reason for dissatisfaction were summarized as ‘difficulty in using a new platform’, ‘increase in study time due to a large amount of assignments’, and ‘issues with the utilization strategy of the platform’. Lastly, learners demanded improvements in the aspects of both the ‘system’ and ‘course design and operation’. Conclusion: Based on the above results, the implications for the implementation of AI-based adaptive learning in universities are as follows. Firstly, it is necessary to consider flexible application options that are tailored to individual universities rather than applying the AI-based adaptive learning platform uniformly. Secondly, adjustments in course design and operation are required considering the implementation of classes utilizing the AI-based adaptive learning platform. Thirdly, further research including various variables is necessary for an in-depth discussion on learning outcomes.

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