Role Modelling in MOOC Discussion Forums

Hecking, Tobias; Chounta, Irene Angelica; Hoppe, H. Ulrich

doi:10.18608/jla.2017.41.6

Cited by 19 publications

(11 citation statements)

References 25 publications

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“…As for forums, they represent an environment of interaction among course learners, where the analysis of information‐giving and information‐seeking relations are taken into account for modeling contributor roles. In this sense, Hecking, Chounta, and Ulrich () generate connection patterns from information exchange networks to identify user roles. Inclusive, they produce semantic roles by identifying topics where a learner seeks information and the areas of interest in which a learner shares information with others.…”

Section: Underlying Factorsmentioning

confidence: 99%

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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Before the emergence of computer‐based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014–2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state‐of‐the‐art and an acumens vision to inspire future labor. This article is categorized under: Application Areas > Education and Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

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Section: Underlying Factorsmentioning

confidence: 99%

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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“…Indeed, it could be corroborated that peer-to-peer learning support was beneficial for both helpers and helpees. Along similar lines, Hecking et al (2017) studied the dynamics of learner profiles in MOOC ("Massive Open Online Courses") forums identifying specific combinations of social role patterns and thematic interactions (help seeking/giving related to certain topics). In the discussion of their findings, already Bull et al (2001) indicated that forming small learning groups with specific task assignments in larger online courses should be beneficial for stimulating engagement and collaborative knowledge building.…”

Section: Introductionmentioning

confidence: 99%

Using Sequence Analysis to Determine the Well-Functioning of Small Groups in Large Online Courses

Hoppe

Doberstein

Hecking

2020

Int J Artif Intell Educ

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Collaborative learning in small groups can enrich and enhance the learning experience in large online courses by facilitating interaction and collaborative knowledge building between peers. on the work reported here addresses scenarios based on asynchronous communication and exchange. As compared to synchronous and face-to-face settings, these scenarios require higher explicit coordination efforts and specific support mechanisms to reach out to inactive group members. This can be achieved by human tutors as well as through automatic interventions by the system in the organization and facilitation of group work. Our approach is based on characterizing the quality or well-functioning of collaboration using sequence alignment techniques that were originally developed in bioinformatics for the comparison of DNA sequences. Sequence alignment provides a similarity measure between pairs of action logs originating from the group work. The ensuing similarity matrix allows for clustering the working groups characterized by the similarity of their complete action logs. Notably, these clusters differ clearly in terms of quality measures related to the activity distribution and group output. This allows for determining quality indicators that can trigger feedback and adaptive scaffolding. Our data stem from inter-university online courses in which regular (weekly or bi-weekly) assignments had to be completed in small groups supported by an online forum for coordination and a separate tool for the actual collaborative writing. The basic actions are interpreted/coded as different contribution types, namely coordination, monitoring, minor/major contributions. Longer periods of inactivity are coded as another type of action (“gap”). These are the building blocks for the sequence alignment. One consequence of our analysis is a revision of the intuitive assumption that inactivity is a main indicator of inefficient group work: We found that inactivity can be compensated by early coordination to the extent that coordination is even the most important factor for successful group work. These insights are now being used to design alert mechanisms to signal inefficient group work and to generate timely interventions.

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“…taking advantage of systems properties to superficially complete the task) [4]. Another stream of work looks at detecting engagement aspects that can be linked to cognition, such as zoning out and mind wandering [5,12], and information seeking/giving [14]. Thirdly, the affective response to instructions (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…We build on the ICAP framework [8] to link cognitive engagement activities to observable behaviors. While ICAP has been used to categorize information seeking in MOOC forums [14,27], its adoption for analyzing video engagement is novel. In our adoption, we have shifted the focus from behavioral aspects derived from interaction log files (e.g.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Comment Types and Levels of Engagement in Video-Based Learning as a Basis for Adaptive Nudging

Taskin

Hecking

Hoppe

et al. 2019

Lecture Notes in Computer Science

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Video is frequently used as a learning medium in a variety of educational settings, including large online courses as well as informal learning scenarios. To foster learner engagement around instructional videos, our learning scenario facilitates interactive note taking and commenting similar to popular social video-sharing platforms. This approach has recently been enriched by introducing nudging mechanisms, which raises questions about ensuing learning effects. To better understand the nature of these effects, we take a closer look at the content of the comments. Our study is based on an ex post analysis of a larger data set from a recent study. As a first step of analysis, video comments are clustered based on a feature set that captures the temporal and semantic alignment of comments with the videos. Based on the ensuing typology of comments, learners are characterized through the types of comments that they have contributed. The results will allow for a better targeting of nudges to improve video-based learning.

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Role Modelling in MOOC Discussion Forums

Cited by 19 publications

References 25 publications

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Using Sequence Analysis to Determine the Well-Functioning of Small Groups in Large Online Courses

Characterizing Comment Types and Levels of Engagement in Video-Based Learning as a Basis for Adaptive Nudging

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