Personalized recommendation of learning content is one of the most frequently cited benefits of personalized online learning. It is expected that with personalized content recommendation students will be able to build their own unique and optimal learning paths and to achieve course goals in the most optimal way. However, in many practical cases students search for learning content not to expand their knowledge, but to address problems encountered in the learning process, such as failures to solve a problem. In these cases, students could be better assisted by remedial recommendations focused on content that could help in resolving current problems. This paper presents a transparent and explainable interface for remedial recommendations in an online programming practice system. The interface was implemented to support SQL programming practice and evaluated in the context of a large database course. The paper summarizes the insights obtained from the study and discusses future work on remedial recommendations.
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Making Educational Recommendations Transparent through a Fine-Grained Open Learner Model
Recommendations for online educational systems generally differ
from recommendations generated in other contexts (e.g. movies,
e-commerce), given that students’ level of knowledge rather then
their interests is key for suggesting the most appropriate content.
Thus, the challenge of making recommendations more transparent
is closely tied to how student skills are estimated and conveyed. In
this paper, we present an approach based on Open Learner Model
visualization as a first step for making the learning content recommendation process more transparent. A preliminary analysis
of students who used the visualization for navigating the content
of an introductory programming course showed that considerable
time was spent exploring the explanatory interface, which could
be linked to the significant likelihood of opening/attempting the
recommended activities.
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- Award ID(s):
- 1822752
- NSF-PAR ID:
- 10111220
- Date Published:
- Journal Name:
- Proceedings of Workshop on Intelligent User Interfaces for Algorithmic Transparency in Emerging Technologies at the 24th ACM Conference on Intelligent User Interfaces, IUI 2019, Los Angeles, USA, March 20, 2019
- Volume:
- 2327
- ISSN:
- 1613-0073
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Cognitive science of learning points to solutions for making use of existing study and instruction time more effectively and efficiently. However, solutions are not and cannot be one-size-fits-all. This paper outlines the danger of overreliance on specific strategies as one-size-fits-all recommendations and highlights instead the cognitive learning processes that facilitate meaningful and long-lasting learning. Three of the most commonly recommended strategies from cognitive science provide a starting point; understanding the underlying processes allows us to tailor these recommendations to implement at the right time, in the right way, for the right content, and for the right students. Recommendations regard teacher training, the funding and incentivizing of educational interventions, guidelines for the development of educational technologies, and policies that focus on using existing instructional time more wisely.
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null (Ed.)ABSTRACT It is difficult in asynchronous online instruction to keep students engaged and motivated. The rapid and unexpected nature of the move to online instruction has meant that the content presented to students has been primarily static and linear. Thus, there is a need for creative pedagogical approaches that re-create some level of the laboratory experience. One economical and accessible approach to building an interactive lab experience is making web-based interactive slides. In the virtual spaces created by this approach, students can explore different modalities of content in a nonlinear and asynchronous manner. We hope that this approach will make the learning process easier and more enjoyable for students while simultaneously making the complex content normally covered in microbiology labs more approachable. In this article we provide detailed instructions for producing web-based interactive slides as well as an example interactive slide that encompasses content that might normally be presented in an introductory microbiology class.more » « less
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Educators can leverage a variety of process models to scaffold students from beginning designer practices to practices aligned with more experienced designers. The Center for Socially Engaged Design at the University of Michigan developed a Socially Engaged Design (SED) Process Model to explicitly emphasize important aspects of design that are often underemphasized or not included in commonly-used design process model visualizations, including, for example, designers embracing the limitations of their own perspective and acknowledging the power they hold, the benefits of integrating contextual considerations, and the use of prototypes throughout a design process rather than as single phase in a design process. To better understand the role of design process models, broadly, and the perceived value of process models that emphasize the importance of people and context in design work, specifically, we investigated upper-level mechanical engineering students' perceptions of this SED Process Model’s visualization. Our findings from this initial exploratory study showed both variability and several consistent themes in participants’ perceptions, for example, there were several interpretations of relationships between different aspects of the model, iteration in design was salient to all participants, and while this SED Process Model’s visualization does have recommendations, several participants noted it does not specify exactly how to achieve those recommendations. Understanding engineering students’ perceptions of this SED Process Model’s visualization can help us (1) iterate on the process model’s visualization and (2) better understand how to leverage multiple process model visualizations in engineering curricula.more » « less
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Purpose This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data.
Design/methodology/approach The researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets.
Findings Pre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them.
Originality/value To the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
