2 research outputs found
Opening Up an Intelligent Tutoring System Development Environment for Extensible Student Modeling
ITS authoring tools make creating intelligent tutoring systems more cost effective, but few authoring tools make it easy to flexibly incorporate an open-ended range of student modeling methods and learning analytics tools. To support a cumulative science of student modeling and enhance the impact of real-world tutoring systems, it is critical to extend ITS authoring tools so they easily accommodate novel student modeling methods. We report on extensions to the CTAT/Tutorshop architecture to support a plug-in approach to extensible student modeling, which gives an author full control over the content of the student model. The extensions enhance the range of adaptive tutoring behaviors that can be authored and support building external, student- or teacher-facing real-time analytics tools. The contributions of this work are: (1) an open architecture to support the plugging in, sharing, re-mixing, and use of advanced student modeling techniques, ITSs, and dashboards; and (2) case studies illustrating diverse ways authors have used the architecture
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μμ μΈκ³΅μ§λ₯ κ΅μ‘μμ€ν
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νμ¬ κ΅μ¬μ μν κ³Ό μΈκ³΅μ§λ₯μ μν μ ꡬλΆνλ μ€κ³βκ°λ° μ°κ΅¬μ μ¬λ‘λ‘μ κ°μΉλ₯Ό μ§λλ€κ³ ν μ μλ€.As the performance and utilization of artificial intelligence technology increases, attempts to apply artificial intelligence to teaching and learning situations are being made. This is because artificial intelligence can diagnose individual characteristics and levels of students, and provide personalized learning materials and feedback. Personalized instruction can provide individualized educational activities so that each student can achieve meaningful learning. In particular, personalized instruction is in high demand in elementary
school mathematics. Mathematics is hierarchical and systemic in content, so it is difficult to proceed with follow-up learning if pre-learning is not done properly. Therefore, personalized instruction according to the characteristics and level of students is necessary. However, there is a realistic limit to meeting the demand for
personalized instruction in numerous classes of elementary schools where one teacher teaches multiple students. A lot of prior research seeks to find the possibility of personalized instruction in technology. What can supplement the realistic limitations of personalized instruction is an artificial intelligence education
system and data-based instructional design. This is because the artificial intelligence education system assists teachers in diagnosing learning, recommending questions, and providing data that can be referred to when designing instructions. However, through previous research, it was confirmed that it is difficult to
practice personalized instruction with only the AI education system and the data-based instructional design itself. Therefore, specific guidelines are needed on how teachers or instructional designers can practice personalized instruction based on data using artificial intelligence education systems. In this context, this study developed instructional design principles that elementary school teachers can refer to when designing and executing data-based personalized instruction using artificial intelligence education systems in mathematics. In this study, to achieve the purpose of the study, 1) developing data-based personalized instruction design principles and detailed guidelines using an artificial intelligence education system, and 2) examining instructors' and learners' responses to the developed instructional design principles. By the design and development research method, this study reviewed prior literature, empirically analyzed needs, derived
initial design principles and detailed guidelines, internal validation (expert review), external validation (applied to the educational field), and derived final design principles and detailed guidelines. First, the initial design principle and detailed guidelines were derived by reviewing the preceding literature and conducting an
empirical search through interviews with two experts for needs analysis. Next, a total of 9 experts reviewed the initial design principles and detailed guidelines for internal validation, and the 3rd design principles and detailed guidelines were derived through revision and supplementation per session. Next, for external
validation, the instruction was conducted by applying the design principle to multiplication of fractions(11H) part of the 2nd semester of the 5th grade in the classroom of one class (24 students) for one month. To this end, the artificial intelligence education system (i-scream Home-Learn) was given to students in advance, and pre-and post-tests were conducted to measure the affective domain of mathematics subject before and after class. While the instruction was in progress, the researcher observed the class five times, and after the class was over, a satisfaction survey was conducted on 24 learners, and interviews were conducted with
one instructor and eight learners. Finally, the final design principle and detailed guidelines were derived based on the results of internal and external validation. The design principles and detailed guidelines developed in this study can be classified into before class, during class, after class, and learning environment aspects, and consist of 10 design principles and 27 detailed guidelines. As a result of interviews with instructors and learners, the instructors were satisfied with the fact that based on the design principles developed in this study, data were collected through the artificial intelligence education system, and instructional design and
learning management were possible based on the data. However, the data that can be referenced for this purpose are limited to the percentage of correct answers per student, percentage of correct answers per question, average daily learning time, and assignment performance rate, and they responded that a lot of
effort was put into the instructional design. Based on the design principles developed in this study, learners are satisfied with the fact that they can identify and supplement their shortcomings by using the AI learning diagnosis, AI question recommendation, and AI dashboard functions of the artificial intelligence education
system. However, they regretted that the personalized feedback during the instruction was not very different from the feedback of the existing instruction, or that there was no time to individually perform personalized learning tasks after class. Both instructors and learners were most satisfied with collecting data during class through the AI education system and using it for instructional design and learning management. In the class satisfaction survey targeting learners, they answered that they were generally satisfied with an average score of 4.28 out of 5 points. As a result of a Wilcoxon Signed Rank Test through pre- and post-surveys that measure the affective domain of mathematics, interest and willingness among the six factors of interest, attitude, value, motivation, willingness, efficacy'. We were able to confirm that two factors were statistically significant.
The period for confirming the effectiveness of the affective domain was short, and as a study of human subjects, it was not possible to conduct a rigorous experimental study, such as not being able to control external variables. However, through the results of the learner interviews, it was confirmed that the
student's efforts were directly reflected in the data, resulting in a willingness to study, or that they felt good and interested because they were praised based on the data. Based on the above research results, the instructional design principles developed in this study were discussed in light of previous studies, and implications were drawn. In addition, follow-up studies were suggested based on the limitations of this study. This study is significant in that it provides a concrete plan for designing a data-based personalized instruction using an artificial intelligence education system in elementary school mathematics classes. In addition, this study can be said to have value as an example of design and development research that differentiates the role of the teacher and the role of artificial intelligence by introducing an artificial intelligence education system into the educational field.I. μλ‘ 1
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