Adaptive Support versus Alternating Worked Examples and Tutored Problems: Which Leads to Better Learning?

Najar, Amir Shareghi; Mitrović, Antonija; McLaren, Bruce M.

doi:10.1007/978-3-319-08786-3_15

Cited by 17 publications

(5 citation statements)

References 15 publications

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“…In the educational data mining (EDM) literature, learning materials are provided in various types, such as problems, examples, videos, and readings. While there have been some studies in the literature on the value of having various types of learning materials for educating students [2,8,33], the relationship between these material types, and their combined effect on student knowledge and student performance is under-investigated.…”

Section: Learning From Multiple Materials Typesmentioning

confidence: 99%

“…Previous research has demonstrated considerable benefits of interacting with multiple types of materials on student learning. For example, worked examples can lead to faster and more effective learning compared to unsupported problem solving [33]; and enriching textbooks with additional forms of content, such as images and videos, increases the helpfulness of learning material [2,1]. Ignoring diverse types of learning materials in student knowledge modeling limits our understanding of how students learn.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Knowledge Acquisition from Multiple Learning Resource Types

Zhao,

Wang,

Sahebi

2020

Preprint

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Students acquire knowledge as they interact with a variety of learning materials, such as video lectures, problems, and discussions. Modeling student knowledge at each point during their learning period and understanding the contribution of each learning material to student knowledge are essential for detecting students' knowledge gaps and recommending learning materials to them. Current student knowledge modeling techniques mostly rely on one type of learning material, mainly problems, to model student knowledge growth. These approaches ignore the fact that students also learn from other types of material. In this paper, we propose a student knowledge model that can capture knowledge growth as a result of learning from a diverse set of learning resource types while unveiling the association between the learning materials of different types. Our multi-view knowledge model (MVKM) incorporates a flexible knowledge increase objective on top of a multi-view tensor factorization to capture occasional forgetting while representing student knowledge and learning material concepts in a lower-dimensional latent space. We evaluate our model in different experiments to show that it can accurately predict students' future performance, differentiate between knowledge gain in different student groups and concepts, and unveil hidden similarities across learning materials of different types.

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Section: Learning From Multiple Materials Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Knowledge Acquisition from Multiple Learning Resource Types

Zhao,

Wang,

Sahebi

2020

Preprint

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“…Studies have shown that such examples are beneficial for the initial acquisition of cognitive skills (Renkl et al, 2002), especially for novice learners (Kirschner et al, 2006). This finding encouraged a range of projects that explored strategies for providing problem-solving support by combining worked-out examples and problems (Gross et al, 2014;McLaren and Isotani, 2011;McLaren et al, 2008McLaren et al, , 2014Najar et al, 2014;van Gog, 2011). However, the number of problems with worked-out examples is usually limited, as creating worked-out examples for each problem requires a lot of authoring effort.…”

Section: Example-based Problem-solving In Intelligent Tutoring Systemsmentioning

confidence: 99%

A study of concept-based similarity approaches for recommending program examples

Hosseini

Brusilovsky

2017

New Review of Hypermedia and Multimedia

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This paper investigates a range of concept-based example recommendation approaches that we developed to provide example-based problem-solving support in the domain of programming. The goal of these approaches is to offer students a set of most relevant remedial examples when they have trouble solving a code comprehension problem where students examine a program code to determine its output or the final value of a variable. In this paper, we use the ideas of semantic-level similaritybased linking developed in the area of intelligent hypertext to generate examples for the given problem. To determine the best-performing approach, we explored two groups of similarity approaches for selecting examples: non-structural approaches focusing on examples that are similar to the problem in terms of concept coverage and structural approaches focusing on examples that are similar to the problem by the structure of the content. We also explored the value of personalized example recommendation based on student's knowledge levels and learning goal of the exercise. The paper presents concept-based similarity approaches that we developed, explains the data collection studies and reports the result of comparative analysis. The results of our analysis showed better ranking performance of the personalized structural variant of cosine similarity approach.

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“…Indeed, research shows that non-assessed learning activities can help students to learn better [11], [12]. But, the realization and attainment of the gained knowledge from the assessed and non-assessed learning materials can be different.…”

Section: Introductionmentioning

confidence: 99%