Journal article
Using a Recommendation System to Support Problem Solving and Case-Based Reasoning Retrieval
Technology, knowledge and learning, Vol.23(1), pp.177-187
04/2018
DOI: 10.1007/s10758-017-9335-y
Abstract
In case library learning environments, learners are presented with an array of narratives that can be used to guide their problem solving. However, according to theorists, learners struggle to identify and retrieve the optimal case to solve a new problem. Given the challenges novice face during case retrieval, recommender systems can be embedded in case libraries to support the decision-making process about which case is most relevant to solve new problems. This emerging technology reports how experts’ assessment of case relevancy was used to retrieve and suggest the most relevant cases for the learner as they engaged in an inquiry-based learning. Specifically, our case library learning system integrates a content-based filtering, which recommends items similar to those a user has selected based on item descriptions or other user data, and is most widely used in textual domains. Implications for practice are also discussed.
Details
- Title: Subtitle
- Using a Recommendation System to Support Problem Solving and Case-Based Reasoning Retrieval
- Creators
- Andrew Tawfik - 0000 0000 9560 654X grid.56061.34 University of Memphis 3798 Walker Ave, Ball Hall, Office - 421D Memphis TN 38152-3570 USAHamed Alhoori - 0000 0000 9003 8934 grid.261128.e Northern Illinois University Dekalb IL USACharles Keene - 0000 0001 2162 3504 grid.134936.a University of Missouri Cornell Hall (Office 422) Columbia MO 65211 USAChristian Bailey - 0000 0000 9003 8934 grid.261128.e Northern Illinois University Dekalb IL USAMaureen Hogan - 0000 0000 9003 8934 grid.261128.e Northern Illinois University Dekalb IL USA
- Resource Type
- Journal article
- Publication Details
- Technology, knowledge and learning, Vol.23(1), pp.177-187
- Publisher
- Springer Netherlands
- DOI
- 10.1007/s10758-017-9335-y
- ISSN
- 2211-1662
- eISSN
- 2211-1670
- Language
- English
- Date published
- 04/2018
- Academic Unit
- Marketing; Bus Admin Undergrad
- Record Identifier
- 9984105917302771
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