Online class size, note reading, note writing and collaborative discourse

Qiu, Mingzhu; Hewitt, Jim; Brett, Clare

doi:10.1007/s11412-012-9151-2

Cited by 30 publications

(19 citation statements)

References 30 publications

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“…Alternatively, perhaps findings on class sizes could lead to implications that would be perceived as a threat to university finances, or pique political sensitivities regarding varying populations' access to equal educational opportunity (Colwell & Jenks, 2004;Curriculum Committee, 2011Diette & Raghav, 2015;Maringe & Sing, 2014;. Whatever the reasons, no existing convergence of research evidence provides guidance for determining optimal online class sizes at different educational levels and under varying contextual conditions-that is, no one size fits all (Beattie & Thiele, 2016;Bettinger, Doss, Loeb, Rogers, & Taylor, 2017;Bristol & Kyarsgaard, 2012;Chapman & Ludlow, 2010;Cheng, 2011;Colwell & Jenks, 2004;Curriculum Committee, 2012;Freeman, 2015;Haynie, 2014;Hewitt & Brett, 2007;Horning, 2007;Johnson, 2010;Kim, 2013;Lee, Dapremont, & Sasser, 2011;Liu, 2012;Mandernach & Holbeck, 2016;Morrison, 2015;Mupinga & Maughan, 2008;Parks-Stamm, Zafonte, & Palenque, 2016;Qiu, Hewitt, & Brett, 2012;Ravenna, 2012;Roby, Ashe, Singh, & Clark, 2013;Seaton & Schwier, 2014;Shaw, 2013;Sorensen, 2014Sorensen, , 2015Taft, Perkowski, & Martin, 2011;Tynan et al, 2015;Walls, 2016;Watson et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…We have experienced the push and pull of revenue pressures conflicting with faculty-perceived goals of quality student learning. University administrators can-and in some settings do-raise enrollment numbers in courses without examining the impact on students' attainment of learning objectives (Maringe & Sing, 2014;Mupinga & Maughan, 2008;Qiu et al, 2012;Snowball, 2014;Tynan et al, 2015). In talking with peers from across the United States, the authors heard the echo of a question that education researchers have asked for decades: What is the right balance between an institution's financial goals in online education courses (i.e., raising enrollment levels) and the quality of the education experience for online students (Colwell & Jenks, 2004)?…”

Section: Introductionmentioning

confidence: 99%

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One Size Does Not Fit All: Toward an Evidence-Based Framework for Determining Online Course Enrollment Sizes in Higher Education

Taft

Kesten

El‐Banna

2019

OLJ

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Class enrollment sizes for online learning in higher education, a topic of persistent interest in the academic literature, impact student learning, pedagogical strategies, school finances, and faculty workload. Yet in the research literature, class size is addressed with insufficient specificity to provide enrollment direction. Seeking guidelines for determining online class sizes, the authors conducted a qualitative research synthesis from 43 recent higher education journals, yielding 58 evidence-based articles. It is clear that no one size fits all. Findings reflect that large classes (≥ 40 students) are effective for foundational and factual knowledge acquisition requiring less individualized faculty-student interaction. Small classes (≤ 15 students) are indicated for courses intending to develop higher order thinking, mastery of complex knowledge, and student skill development. Pedagogical intent should dictate class size. Using well-established learning theories, the authors describe current understandings of online enrollments and propose an analytical framework for pedagogically driven, numerically specific class sizes. Highlights:• There is academic interest in online course sizes in higher education.• Research indicates "no one size fits all" online classes.• Class sizes should be based on learning level and identified pedagogical intent.• Large classes are appropriate for foundation-level learning.• Small classes are appropriate for learning requiring higher order thinking.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

One Size Does Not Fit All: Toward an Evidence-Based Framework for Determining Online Course Enrollment Sizes in Higher Education

Taft

Kesten

El‐Banna

2019

OLJ

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“…As MOOCs proliferate in 2013, a natural question is: How can we better understand MOOC forum activities through the large-scale, extensive data emerging in the recent months? It has been observed that such forums suffered from the following major problems [17,23]:…”

Section: Introductionmentioning

confidence: 99%

Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model

Brinton

Chiang

Jain

et al. 2014

IEEE Trans. Learning Technol.

195

109

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We study user behavior in the courses offered by a major Massive Online Open Course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable education in MOOCs and is done via online discussion forums, our main focus is in understanding forum activities. Two salient features of MOOC forum activities drive our research: 1. High decline rate: for all courses studied, the volume of discussions in the forum declines continuously throughout the duration of the course. 2. High-volume, noisy discussions: at least 30% of the courses produce new discussion threads at rates that are infeasible for students or teaching staff to read through. Furthermore, a substantial portion of the discussions are not directly course-related.We investigate factors that correlate with the decline of activity in the online discussion forums and find effective strategies to classify threads and rank their relevance. Specifically, we use linear regression models to analyze the time series of the count data for the forum activities and make a number of observations, e.g., the teaching staff's active participation in the discussion increases the discussion volume but does not slow down the decline rate. We then propose a unified generative model for the discussion threads, which allows us both to choose efficient thread classifiers and design an effective algorithm for ranking thread relevance. Our ranking algorithm is further compared against two baseline algorithms, using human evaluation from Amazon Mechanical Turk.

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“…[4] One of the significant factors in explaining the possible impact of active online learning is group size. [7] Generally, many researchers prefer smaller groups because they make possible an expanded range of tasks and responsibilities for each member of a group. According to Gagne, Wager, Golas, and Keller, smaller groups also support active learning-enabling group members to work as active participants in the process of data gathering, organizing and synthesizing the data, and thereby establishing knowledge.…”

Section: Group Size and Collaborative Learningmentioning

confidence: 99%

Student perceptions of group experience in online RN-to-BSN research course

Kim‐Godwin

Martínez

2016

JNEP

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Objective: The overall aim of this pilot study was to explore students' perception of teaching, social, cognitive and learner presence within an RN to BSN asynchronous online nursing research course. Methods: This descriptive correlational study used an online survey. Students' perceptions of learning effectiveness regarding group size and class size were assessed with the Revised Community of Inquiry (RCOI) Conclusions:The findings of the current study imply that learning effectiveness was not associated with group sizes or class sizes. However, the findings suggest that the use of group collaboration promotes teaching, social, cognitive, and learner presence.

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Online class size, note reading, note writing and collaborative discourse

Cited by 30 publications

References 30 publications

One Size Does Not Fit All: Toward an Evidence-Based Framework for Determining Online Course Enrollment Sizes in Higher Education

One Size Does Not Fit All: Toward an Evidence-Based Framework for Determining Online Course Enrollment Sizes in Higher Education

Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model

Student perceptions of group experience in online RN-to-BSN research course

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