Crowd Guilds

Massive open online courses (MOOCs) have considered to be a potential to disseminate engineering education at scale. However, the pedagogical requirement demanded engineering student to equip with specific skillsets such as communication, interactivity, and collaboration. Yet, MOOCs are skeptical of facilitating those skills. Generally, MOOCs evaluated against accessibility and usability. These evaluations may not perceive whether the platform designs in MOOCs support to much-needed interactions and collaboration required in engineering education. We evaluated 6 MOOC platform designs under the lens of collaboration and types of interactions which occur as learner to learner, learner to instructor, learner to platform and learner to content. Evaluation criteria were derived us-ing previous frameworks and evaluations were conducted using 10 participants. Based on each criterion a matrix was formed with a weight to compare results. We found overall collaboration opportunities in platforms are significantly less than the opportunities to interact. Based on the findings we provide 5 design recommendations and provide a future direction for MOOC platform designs which en-ables facilitation to engineering education through MOOCs.

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“…These tools were empirically tested and validated to evaluate than many other conceptual frameworks. First tool consists of criteria of: [41].…”

Section: Mooc Evaluation Criteria and Toolsmentioning

confidence: 99%

“…Interactivity and Collaboration in a MOOC is identified as student's success factors in many research works [13,41]. In order to evaluate the MOOC platform designs, we are required to select number of MOOC platforms and an evaluation criterion.…”

Section: Evaluation Of Mooc Platform Designsmentioning

confidence: 99%

MOOCs Lack Interactivity and Collaborativeness: Evaluating MOOC Platforms

2020

Self Cite

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“…Competitive self-organization on which the SOPs approach is based, is in line with the main idea of the aforementioned paper, in that it also incentivizes workers to select others based on actual performance and compatibility, as this would help them win. As a second built-in quality mechanism, the SOPs framework uses peer assessment to identify winning stories; a powerful mechanism to enable collective feedback and emerging selectivity (Whiting et al, 2017). Competitive self-organisation and peer assessment complement the importance of agency within the SOPs framework .…”

Section: Resultsmentioning

confidence: 99%

“…From a task point-of-view, the story peer assessment at this stage allows for a collective decision to emerge regarding the outcome of the task, that is, users collectively have full control over the task result. Peer review is also a proven way of incorporating quality assurance during the task (Whiting et al, 2017). Alternative ways of evaluating the team result after each collaboration round can be envisioned and they are relatively straightforward to incorporate, without affecting the core of the proposed system.…”

Section: Methodsmentioning

confidence: 99%

Self-organization in online collaborative work settings

Lykourentzou

Vinella

Ahmed

et al. 2022

Collective Intelligence

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As the volume and complexity of distributed online work increases, collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of online collaborations by grouping workers in a top-down fashion and according to a set of predefined decision criteria. This approach often means that workers have little say in the collaboration formation process. Depriving users of control over whom they will work with can stifle creativity and initiative-taking, increase psychological discomfort, and, overall, result in less-than-optimal collaboration results—especially when the task concerned is open-ended, creative, and complex. In this work, we propose an alternative model, called Self-Organizing Pairs (SOPs), which relies on the crowd of online workers themselves to organize into effective work dyads. Supported but not guided by an algorithm, SOPs are a new human-centered computational structure, which enables participants to control, correct, and guide the output of their collaboration as a collective. Experimental results, comparing SOPs to two benchmarks that do not allow user agency, and on an iterative task of fictional story writing, reveal that participants in the SOPs condition produce creative outcomes of higher quality, and report higher satisfaction with their collaboration. Finally, we find that similarly to machine learning-based self-organization, human SOPs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible collaborators.

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“…In our case, we can expose the controversy or disagreement about a causal relation at the time of the experiment by looking at the networks already created by others. Another potential solution is enabling peer review [77], allowing crowdworkers to provide feedback to each other. Other potential solutions include automatically extracting digestible scientific documents related to the relevant causal attributes.…”

Section: Reflecting On the User Studiesmentioning

confidence: 99%

Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations

Salim,

Hoque,

Mueller

2024

Proc. ACM Hum.-Comput. Interact.

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Causal belief is a cognitive practice that humans apply everyday to reason about cause and effect relations between factors, phenomena, or events. Like optical illusions, humans are prone to drawing causal relations between events that are only coincidental (i.e., causal illusions). Researchers in domains such as cognitive psychology and healthcare often use logistically expensive experiments to understand causal beliefs and illusions. In this paper, we propose Belief Miner, a crowdsourcing method for evaluating people's causal beliefs and illusions. Our method uses the (dis)similarities between the causal relations collected from the crowds and experts to surface the causal beliefs and illusions. Through an iterative design process, we developed a web-based interface for collecting causal relations from a target population. We then conducted a crowdsourced experiment with 101 workers on Amazon Mechanical Turk and Prolific using this interface and analyzed the collected data with Belief Miner. We discovered a variety of causal beliefs and potential illusions, and we report the design implications for future research.

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Crowd Guilds

Cited by 38 publications

References 60 publications

MOOCs Lack Interactivity and Collaborativeness: Evaluating MOOC Platforms

MOOCs Lack Interactivity and Collaborativeness: Evaluating MOOC Platforms

Self-organization in online collaborative work settings

Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations

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