Characterizing MOOC Pedagogies: Exploring Tools and Methods for Learning Designers and Researchers

Quintana, Rebecca M.; Tan, Yuanru

doi:10.24059/olj.v23i4.2084

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…Researchers can start by collecting LOs (Table 1) from the MOOCs providers, Coursera, and edX, and then manually annotate them based on Bloom's taxonomy action verbs list. However, this could lead to ambiguity about the actual meaning of the required cognition [17][18][19]. ISSN 2664-9969 3.2.…”

Section: Research Results and Discussionmentioning

confidence: 99%

Development of force and communication management methodology using resource optimization methods in military (force) operations

Shyshatskyi

Левченко

Trotsko

et al. 2022

TAPR

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The object of research is massive open online courses. One of the most problematic areas in online learning is how to improve the quality assurance of digital learning systems. Analysis and classification of massive open online courses is a difficult task, given the variability of massive open online courses structures, contents, designs, platforms, providers, and learner profiles. To overcome this challenge, this study aims to propose an automatic and large-scale machine learning based classification system for massive open online courses according to their learning objectives by making use of the six cognitive levels of Bloom’s taxonomy. During the course of the research, it is shown that analyzing learning objectives associated with modules and programs can further enhance the quality of digital learning system. As a result of the research, a representation and a detailed analysis of the dataset for experimentation with the different models are provided. Further research can focus on the privacy implications of the current control on developments of artificial intelligence taking into account creativity, and innovation which can hardly be performed by machines.

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Section: Research Results and Discussionmentioning

confidence: 99%

Development of force and communication management methodology using resource optimization methods in military (force) operations

Shyshatskyi

Левченко

Trotsko

et al. 2022

TAPR

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“…By affording some pedagogical interactions and foreclosing others, educational platforms silently condition instructors and students towards norms of teaching and learning. When instructors and students work outside the limitations of a given platform, they must invent workaround procedures, or acquiesce to the conditions set by their platform (Quintana & Tan, 2019). In this way, platforms function pedagogically by guiding instructors and students towards particular ways of knowing, doing, and being.…”

Section: Platformization As Public Pedagogymentioning

confidence: 99%

Conceptions of Time in Educational Technology

Fortman,

Quintana,

Aguinaga

2023

OLJ

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The adoption of technology-enhanced online learning platforms is transforming teaching and learning practices within and outside the university. As online learning and educational technology become increasingly ubiquitous, there is a need for equity-minded scholarship attending to the social, cultural, and political implications of the technology sustaining online learning. While prior literature has made important strides framing education technology within conversations of equity and justice, there is a lack of empirical research analyzing marketing material of education technology. This presents a significant gap in understanding for education researchers, as marketing material plays a significant role in shaping public perceptions of technology, and can be widely read among students, instructors, and university stakeholders before directly engaging with the tool. Given recent scholarly interest in the ways subjective understandings of temporality are implicated in learning design, the present study connects burgeoning interest in temporality towards corporate marketing material of learning design. Drawing on artifact analysis methods, we analyzed blog posts from Coursera and customer success stories from Microsoft that describe how their products are designed to support online learning. Our research questions include: (1) How does marketing material from two education technology companies shape subjective understandings of temporality in online learning? (2) How can these temporal representations be leveraged to promote equity-oriented pedagogical design? Results from our analysis show how time is constituted as an efficient and agentic resource, and as an orientation towards future careers. We discuss how these findings have implications for equity-oriented pedagogical design by linking conceptions of time to neoliberalism and humanization.

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“…The rating scale was from 0 to 5, with zero being the lowest and five being the highest; Quintana and Tan (2019) developed a more advanced version of the AMP tool, complete with new language and indications. They demonstrated how machine learning with the KNN method could discover pedagogically comparable MOOCs after analyzing 20 MOOCs (from the same platform and institution but different topics).…”

Section: Related Workmentioning

confidence: 99%

An explainable attention-based bidirectional GRU model for pedagogical classification of MOOCs

Sebbaq

Faddouli

2022

ITSE

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Purpose The purpose of this study is, First, to leverage the limitation of annotated data and to identify the cognitive level of learning objectives efficiently, this study adopts transfer learning by using word2vec and a bidirectional gated recurrent units (GRU) that can fully take into account the context and improves the classification of the model. This study adds a layer based on attention mechanism (AM), which captures the context vector and gives keywords higher weight for text classification. Second, this study explains the authors’ model’s results with local interpretable model-agnostic explanations (LIME). Design/methodology/approach Bloom's taxonomy levels of cognition are commonly used as a reference standard for identifying e-learning contents. Many action verbs in Bloom's taxonomy, however, overlap at different levels of the hierarchy, causing uncertainty regarding the cognitive level expected. Some studies have looked into the cognitive classification of e-learning content but none has looked into learning objectives. On the other hand, most of these research papers just adopt classical machine learning algorithms. The main constraint of this study is the availability of annotated learning objectives data sets. This study managed to build a data set of 2,400 learning objectives, but this size remains limited. Findings This study’s experiments show that the proposed model achieves highest scores of accuracy: 90.62%, F1-score and loss. The proposed model succeeds in classifying learning objectives, which contain ambiguous verb from the Bloom’s taxonomy action verbs, while the same model without the attention layer fails. This study’s LIME explainer aids in visualizing the most essential features of the text, which contributes to justifying the final classification. Originality/value In this study, the main objective is to propose a model that outperforms the baseline models for learning objectives classification based on the six cognitive levels of Bloom's taxonomy. In this sense, this study builds the bidirectional GRU (BiGRU)-attention model based on the combination of the BiGRU algorithm with the AM. This study feeds the architecture with word2vec embeddings. To prove the effectiveness of the proposed model, this study compares it with four classical machine learning algorithms that are widely used for the cognitive classification of text: Bayes naive, logistic regression, support vector machine and K-nearest neighbors and with GRU. The main constraint related to this study is the absence of annotated data; there is no annotated learning objective data set based on Bloom’s taxonomy's cognitive levels. To overcome this problem, this study seemed to have no choice but to build the data set.

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Characterizing MOOC Pedagogies: Exploring Tools and Methods for Learning Designers and Researchers

Cited by 10 publications

References 19 publications

Development of force and communication management methodology using resource optimization methods in military (force) operations

Development of force and communication management methodology using resource optimization methods in military (force) operations

Conceptions of Time in Educational Technology

An explainable attention-based bidirectional GRU model for pedagogical classification of MOOCs

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