Depth of Use: An Empirical Framework to Help Faculty Gauge the Relative Impact of Learning Management System Tools

Hassan, Taha; Edmison, Bob; Cox, Larry; Louvet, Matt; Williams, Daron; McCrickard, D. Scott

doi:10.1145/3341525.3387375

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…However, there is a pressing need for the educational RS community to recognize platform-level changes in the domain. Recommender systems have to reckon with concerns of efficacy, trust and interpretation for multiple stakeholders, and at scale [14], especially as service-based learning management systems (LMS) become the primary infrastructure for productivity, communication and class management at institutions of higher learning [22]. Figure 2 describes five desirable aspects of RS output (relevance, trust, diversity, serendipity, and interpretation), and how faculty members and students who participated in our study felt about their relative importance.…”

Section: Editorial Authority Trust and Algorithmic Agencymentioning

confidence: 99%

Learning to Trust: Understanding Editorial Authority and Trust in Recommender Systems for Education

Hassan,

Edmison,

Stelter

et al. 2021

Preprint

Self Cite

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Trust in a recommendation system (RS) is often algorithmically incorporated using implicit or explicit feedback of user-perceived trustworthy social neighbors, and evaluated using user-reported trustworthiness of recommended items. However, real-life recommendation settings can feature group disparities in trust, power, and prerogatives. Our study examines a complementary view of trust which relies on the editorial power relationships and attitudes of all stakeholders in the RS application domain. We devise a simple, first-principles metric of editorial authority, i.e., user preferences for recommendation sourcing, veto power, and incorporating user feedback, such that one RS user group confers trust upon another by ceding or assigning editorial authority. In a mixed-methods study at Virginia Tech, we surveyed faculty, teaching assistants, and students about their preferences of editorial authority, and hypothesis-tested its relationship with trust in algorithms for a hypothetical 'Suggested Readings' RS. We discover that higher RS editorial authority assigned to students is linked to the relative trust the course staff allocates to RS algorithm and students. We also observe that course staff favors higher control for the RS algorithm in sourcing and updating the recommendations long-term.Using content analysis, we discuss frequent staff-recommended student editorial roles and highlight their frequent rationales, such as perceived expertise, scaling the learning environment, professional curriculum needs, and learner disengagement. We argue that our analyses highlight critical user preferences to help detect editorial power asymmetry and identify RS use-cases for supporting teaching and research.

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Section: Editorial Authority Trust and Algorithmic Agencymentioning

confidence: 99%

Learning to Trust: Understanding Editorial Authority and Trust in Recommender Systems for Education

Hassan,

Edmison,

Stelter

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In essence, micro-teaching is a flexible application of projectbased teaching, which is to transform the project tasks in actual work into learning-type micro-theme tasks [16]. Realizing it in a series of teaching practice activities based on class hours can better between teachers and students and between students [17].…”

Section: Introductionmentioning

confidence: 99%

Design and Management of Micro-Teaching Mode of Education in Colleges Driven by Big Data

Guo

2023

Wireless Communications and Mobile Computing

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Big data has become a new driving force for national innovation and entrepreneurship. Although colleges and universities, which are responsible for cultivating high-quality entrepreneurship and innovation talents, have achieved certain results, there are still many problems in practice, which need to be driven by big data. Big data and the practice of innovation and entrepreneurship education in colleges and universities have certain inherent commonality. The integration of big data into the practice of innovation and entrepreneurship education in colleges and universities needs to be improved and strengthened in terms of top-level design, data environment, and educational concepts. Educational big data has become a hot topic and trend in educational research. College students’ entrepreneurship is a kind of entrepreneurial process in which special groups of college students and graduate students are the main body of entrepreneurship. With the recent transformation process of our country and the increasing pressure of social employment, entrepreneurship has gradually become a career choice for college students and graduates. Through data mining, statistical analysis, model construction, and comprehensive reasoning of educational big data are realized, and positive suggestions and countermeasures are provided for education and teaching. On the basis of the pattern analysis of educational data mining process, the concrete application of educational data mining is analyzed, and the correlation and rules between educational phenomena are found, so as to provide educational prediction and educational decision support for further optimization of teaching.

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Data-Informed Learning Design in a Computer Science Course

Williams,

Cox,

Ellis

et al. 2023

Learning, Design, and Technology

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Depth of Use: An Empirical Framework to Help Faculty Gauge the Relative Impact of Learning Management System Tools

Cited by 6 publications

References 12 publications

Learning to Trust: Understanding Editorial Authority and Trust in Recommender Systems for Education

Learning to Trust: Understanding Editorial Authority and Trust in Recommender Systems for Education

Design and Management of Micro-Teaching Mode of Education in Colleges Driven by Big Data

Data-Informed Learning Design in a Computer Science Course

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