Student cognitive modeling is a fundamental task in the intelligence education field.It serves as the basis for various downstream applications,such as student profiling,personalized educational content recommendation...Student cognitive modeling is a fundamental task in the intelligence education field.It serves as the basis for various downstream applications,such as student profiling,personalized educational content recommendation,and adaptive testing.Cognitive Diagnosis(CD)and Knowledge Tracing(KT)are two mainstream categories for student cognitive modeling,which measure the cognitive ability from a limited time(e.g.,an exam)and the learning ability dynamics over a long period(e.g.,learning records from a year),respectively.Recent efforts have been dedicated to the development of open-source code libraries for student cognitive modeling.However,existing libraries often focus on a particular category and overlook the relationships between them.Additionally,these libraries lack sufficient modularization,which hinders reusability.To address these limitations,we have developed a unified PyTorch-based library EduStudio,which unifies CD and KT for student cognitive modeling.The design philosophy of EduStudio is from two folds.From a horizontal perspective,EduStudio employs the modularization that separates the main step pipeline of each algorithm.From a vertical perspective,we use templates with the inheritance style to implement each module.We also provide eco-services of EduStudio,such as the repository that collects resources about student cognitive modeling and the leaderboard that demonstrates comparison among models.Our open-source project is available at the website of edustudio.ai.展开更多
Career indecision is a difficult obstacle confronting adolescents. Traditional vocational assessment research measures it by means of questionnaires and diagnoses the potential sources of career indecision. Based on t...Career indecision is a difficult obstacle confronting adolescents. Traditional vocational assessment research measures it by means of questionnaires and diagnoses the potential sources of career indecision. Based on the diagnostic outcomes, career counselors develop treatment plans tailored to students. However, because of personal motives and the architecture of the mind, it may be difficult for students to know themselves, and the outcome of questionnaires may not fully reflect their inner states and statuses. Selfperception theory suggests that students＇ behavior could be used as a clue for inference. Thus, we proposed a data-driven framework for forecasting student career choice upon graduation based on their behavior in and around the campus, thereby playing an important role in supporting career counseling and career guidance. By evaluating on 10M behavior data of over four thousand students, we show the potential of this framework for this functionality.展开更多
基金supported in part by grants from the National Science and Technology Major Project,China(Grant No.2021ZD0111802)the National Natural Science Foundation of China(Grant Nos.72188101,62406096,and 62376086)the Fundamental Research Funds for the Central Universities,China(Grant No.JZ2024HGQB0093).
文摘Student cognitive modeling is a fundamental task in the intelligence education field.It serves as the basis for various downstream applications,such as student profiling,personalized educational content recommendation,and adaptive testing.Cognitive Diagnosis(CD)and Knowledge Tracing(KT)are two mainstream categories for student cognitive modeling,which measure the cognitive ability from a limited time(e.g.,an exam)and the learning ability dynamics over a long period(e.g.,learning records from a year),respectively.Recent efforts have been dedicated to the development of open-source code libraries for student cognitive modeling.However,existing libraries often focus on a particular category and overlook the relationships between them.Additionally,these libraries lack sufficient modularization,which hinders reusability.To address these limitations,we have developed a unified PyTorch-based library EduStudio,which unifies CD and KT for student cognitive modeling.The design philosophy of EduStudio is from two folds.From a horizontal perspective,EduStudio employs the modularization that separates the main step pipeline of each algorithm.From a vertical perspective,we use templates with the inheritance style to implement each module.We also provide eco-services of EduStudio,such as the repository that collects resources about student cognitive modeling and the leaderboard that demonstrates comparison among models.Our open-source project is available at the website of edustudio.ai.
基金This work was supported by the National Natural Science Foundation of China （Grant Nos. 61502077, 61631005） and the Fundamental Research Funds for the Central Universities （ZYGX2014Z012）.
文摘Career indecision is a difficult obstacle confronting adolescents. Traditional vocational assessment research measures it by means of questionnaires and diagnoses the potential sources of career indecision. Based on the diagnostic outcomes, career counselors develop treatment plans tailored to students. However, because of personal motives and the architecture of the mind, it may be difficult for students to know themselves, and the outcome of questionnaires may not fully reflect their inner states and statuses. Selfperception theory suggests that students＇ behavior could be used as a clue for inference. Thus, we proposed a data-driven framework for forecasting student career choice upon graduation based on their behavior in and around the campus, thereby playing an important role in supporting career counseling and career guidance. By evaluating on 10M behavior data of over four thousand students, we show the potential of this framework for this functionality.
