Uncovering Latent Knowledge: A Comparison of Two Algorithms

Lynch, Danny; Howlin, Colm

doi:10.1007/978-3-319-08786-3_32

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…In order to overcome the complexity of O(2 K ) of the DINA model, some knowledge representations such as Attribute Hierarchy Model [16,17] or Knowledge Space Theory [18,19] have been devised, relying on dependencies over KCs in the form of a directed acyclic graph. We would like to compare these approaches to GenMA.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Testing Using a General Diagnostic Model

Vie¹,

Popineau²,

Bourda³

et al. 2016

Adaptive and Adaptable Learning

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In online learning platforms such as MOOCs, computerized assessment needs to be optimized in order to prevent boredom and dropout of learners. Indeed, they should spend as little time as possible in tests and still receive valuable feedback. It is actually possible to reduce the number of questions for the same accuracy with computerized adaptive testing (CAT): asking the next question according to the past performance of the examinee. CAT algorithms are divided in two categories: summative CATs, that measure the level of examinees, and formative CATs, that provide feedback to the examinees at the end of the test by specifying which knowledge components need further work. In this paper, we formalize the problem of test-size reduction by predicting student performance, and propose a new hybrid CAT algorithm GenMA based on the general diagnostic model, that is both summative and formative. Using real datasets, we compare our model to popular CAT models and show that GenMA achieves better accuracy while using fewer questions than the existing models.

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Section: Discussionmentioning

confidence: 99%

Adaptive Testing Using a General Diagnostic Model

Vie¹,

Popineau²,

Bourda³

et al. 2016

Adaptive and Adaptable Learning

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show abstract

“…The goal is to provide suitable and diverse learning materials and learning paths based on learners' characteristics (e.g., behaviors, preferences, styles and prior knowledge) [ 20 ]. To achieve this goal, the content model, learner model [ 21 ], tutoring model [ 22 ] and adaptive engine [ 23 ] are types of ALS. Some studies have explored the factors that affect students' usage intentions, including whether students have a positive learning experience [ 24 ], whether students find feedback from the system beneficial to their learning [ 25 ], the length of time they use the system [ 26 ], the clarity of the instructional objectives designed in the system, the alignment of instructional resources and the assessment [ 27 ], and incentive alignment [ 28 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Continue using or gathering dust? A mixed method research on the factors influencing the continuous use intention for an AI-powered adaptive learning system for rural middle school students

Han,

Liu,

Liu

et al. 2024

Heliyon

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“…This approach relies on analyzing data, such as student performance and behavior, to make recommendations and personalize the learning experience. On the other hand, knowledge engineering methods, such as the knowledge vector [38,39], involve a comprehensive analysis of the exercises solved by the student. This analysis considers the common misconceptions and specific difficulties faced by the student, and other factors necessary for designing hints and feedback messages.…”

Section: Related Workmentioning

confidence: 99%

“…The knowledge vector method is widely recognized in education as a way to organize the learning process and reduce the student's effort by targeting areas that require further mastery [39]. In our platform, we have implemented this approach by highlighting a graph that represents a typical knowledge vector in a specific subject area.…”

Section: Related Workmentioning

confidence: 99%

Methods and Tools Supporting the Learning and Teaching of Mathematics Dedicated to Students with Blindness

et al. 2023

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Teaching mathematics to blind people is a challenge of modern educational methods. This article presents a method of preparing the adapted material and its usage in the learning process of mathematics by blind people, as well as the results of evaluating the proposed approach. The presented results were obtained based on a mathematical analysis course conducted in two classes—with and without using the developed method. The developed method uses the conceptualization of knowledge as a graph. The learning process is supported by feedback processes that consider the mechanisms of knowledge and error vectors, on which a personalized adaptation of the learning path is made for each particular student. The evaluation process has shown a statistically significant improvement in learning results achieved by blind students. The average final test score in the group working with the platform during learning increased by 14%. In addition, there was an increase in cooperation between blind students who had the opportunity to take on the role of a teacher, which was observed in 27% of the participants. Our results indicate the effectiveness of the developed approach and motivate us to evaluate the method in a broader group of students. The engagement of students indirectly indicates overcoming the barriers known from the state of the art: uncertainty, poor motivation, and difficulties in consolidating the acquired skills.

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Uncovering Latent Knowledge: A Comparison of Two Algorithms

Cited by 4 publications

References 3 publications

Adaptive Testing Using a General Diagnostic Model

Adaptive Testing Using a General Diagnostic Model

Continue using or gathering dust? A mixed method research on the factors influencing the continuous use intention for an AI-powered adaptive learning system for rural middle school students

Methods and Tools Supporting the Learning and Teaching of Mathematics Dedicated to Students with Blindness

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