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Record W2167351144 · doi:10.19173/irrodl.v6i2.237

Structure, Content, Delivery, Service, and Outcomes: Quality e-Learning in higher education

2005· article· en· W2167351144 on OpenAlexaffvenue
Colla J. MacDonald, Terrie Lynn Thompson

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalUniversité LavalUniversity of Ottawa
Fundersnot available
KeywordsCarrQuality (philosophy)Experiential learningKnowledge managementPsychologyInstructional designComputer sciencePedagogy

Abstract

fetched live from OpenAlex

<P>This paper addresses the need for quality e-Learning experiences. We used the Demand-Driven Learning Model (MacDonald, Stodel, Farres, Breithaupt, and Gabriel, 2001) to evaluate an online Masters in Education course. Multiple data collection methods were used to understand the experiences of stakeholders in this case study: the learners, design team, and facilitators. We found that all five dimensions of the model (structure, content, delivery, service, and outcomes) must work in concert to implement a quality e-Learning course. Key themes include evolving learner needs, the search for connection, becoming an able e-participant, valued interactions, social construction of content, integration of delivery partners, and mindful weighing of benefits and trade-offs. By sharing insights into what is needed to design and deliver an e-Learning experience, our findings add to the growing knowledge of online learning. Using this model to evaluate perceptions of quality by key stakeholders has led to insights and recommendations on the Demand Driven Learning Model itself which may be useful for researchers in this area and strengthen the model.</P> <P>Quality has been defined in terms of the design of the e-Learning experience, the contextualized experience of learners, and evidence of learning outcomes (Carr and Carr, 2000; Jung 2000; Salmon, 2000). Quality and design of e-Learning courses, however, are sometimes compromised in an “ . . . effort to simply get something up and running” in response to pressing consumer demands (Dick, 1996, p. 59). Educators and researchers have voiced concern over the lack of rigorous evaluation studies of e-Learning programs (e.g., Arbaugh, 2000; Howell, Saba, Lindsay, and Williams, 2004; Lockyer, Patterson, and Harper, 1999; Robinson, 2001). McGorry (2003) adds, “although the number of courses being delivered via the Internet is increasing rapidly, our knowledge of what makes these courses effective learning experiences is limited” (p. 160). In an economic environment marked by intensive competition between educational institutions, producing and ensuring quality e-Learning programs will be a competitive advantage to attract learners to post secondary institutions (Daniel, 1996; Duderstadt, 1999).</P> <P>In this study we used a credible model, the Demand-Driven Learning Model (DDLM), (MacDonald, Stodel, Farres, Breithaupt, and Gabriel, 2001) and its companion evaluation tool (MacDonald, Breithaupt, Stodel, Farres, and Gabriel, 2002) to design and evaluate an online course. Several data collection methods were used to understand the experiences of key stakeholders in this case study: learners, design team, and facilitators. In addition to adding to the growing knowledge of online learning, our findings highlight additional elements that could be incorporated into the DDLM to further refine the model.</P>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.194
GPT teacher head0.506
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations93
Published2005
Admission routes2
Has abstractyes

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