What's In It for the Learners? Evidence from a Randomized Field Experiment on Learnersourcing Questions in a MOOC

Singh, Anjali; Brooks, Christopher; Lin, Yen-Hui; Li, Warren

doi:10.1145/3430895.3460142

Cited by 15 publications

(5 citation statements)

References 28 publications

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“…An investigation of which of these versions most helped students in testing-related exam questions yielded no significant differences [64]. A similar result has later been observed by Singh et al [55] who found no significant differences between students who created MCQs and those who only answered them.…”

Section: Student Learningsupporting

confidence: 74%

“…However, in very large courses where even a small proportion of active students can generate a repository of a useful size, there may be value in allowing students to choose whether to create or simply use the generated resources. In a very recent exploration of this idea in the context of a MOOC, Singh et al find that when content creation is optional, learners perceive more value in creating questions and create higher quality questions than when content generation is required [55].…”

Section: B Engaging Studentsmentioning

confidence: 99%

“…In theory, students can benefit from both creating resources, and using the resources created by others (as discussed in Subsection II-B). There is a growing body of evidence in favour of both of these activities, although there is less focus in the literature on the benefits of generating resources [55].…”

Section: Student Learningmentioning

confidence: 99%

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Lessons Learned From Four Computing Education Crowdsourcing Systems

et al. 2023

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Crowdsourcing is a general term that describes the practice of many individuals working collectively to achieve a common goal or complete a task, often involving the generation of content. In an educational context, crowdsourcing of learning materials -where students create resources that can be used by other learners -offers several benefits. Students benefit from the act of producing resources as well as from using the resources. Despite benefits, instructors may be hesitant to adopt crowdsourcing for several reasons, such as concerns around the quality of content produced by students and the perceptions students may have of creating resources for their peers. While prior work has explored crowdsourcing concerns within the context of individual tools, lessons that are generalisable across multiple platforms and derived from practical use can provide considerably more robust insights. In this perspective article, we present four crowdsourcing tools that we have developed and used in computing classrooms. From our previous studies and experience, we derive lessons which shed new light on some of the concerns that are typical for instructors looking to adopt such tools. We find that across multiple contexts, students are capable of generating high quality learning content which provides good coverage of key concepts. Although students do appear hesitant to engage with new kinds of activities, various types of incentives have proven effective. Finally, although studies on learning effects have shown mixed results, no negative outcomes have been observed. In light of these lessons, we hope to see a greater uptake and use of crowdsourcing in computing education.INDEX TERMS contributing student pedagogy, crowdsourcing, crowdsourcing systems, learnersourcing

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Section: Student Learningsupporting

confidence: 74%

Section: B Engaging Studentsmentioning

confidence: 99%

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Lessons Learned From Four Computing Education Crowdsourcing Systems

et al. 2023

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“…Pese a estos datos, que permiten valorar la pertinencia del diseño y contenido propuestos, los resultados alcanzados reclaman la necesidad de reforzar el sentido práctico de estos cursos, orientándolos hacia una empleabilidad de los aprendizajes a partir de un diseño innovador que responda a las necesidades de los alumnos, tal como defienden Liu et al (2019). En este sentido, aprovechar las ventajas de la tecnología y tomar como base los principios del Diseño Universal de Aprendizaje (DUA) (Al-Azawei et al, 2017) facilitaría la puesta en marcha de diferentes alternativas de acceso al conocimiento y permitiría a los alumnos definir el grado de implicación que desean establecer con el curso, pudiendo adoptar incluso el rol de creador de recursos, ideas y soluciones, tal como abogan Abdi et al (2021) o Singh et al (2021), entre otros.…”

Section: Conclusiones Y Discusionesunclassified

Criterios de calidad de un MOOC basado en la valoración de los estudiantes

Segovia-García¹

2021

Bordón

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INTRODUCCIÓN. El creciente interés por el aprendizaje abierto y a distancia de los últimos años ha hecho que estudiantes de todo el mundo puedan acceder y aprender a través de cursos gratuitos en línea ofrecidos por conocidas universidades. Sin embargo, a pesar de los potenciales beneficios de los MOOC, sus tasas de abandono son muy elevadas y su calidad general es cuestionable, y actualmente está siendo objeto de un intenso debate. El objetivo de este estudio es conocer la opinión de los estudiantes una vez finalizados los MOOC y detectar mejoras que permitan incrementar la calidad de los cursos. MÉTODO. A partir del análisis de diferentes herramientas para medir la calidad de los sistemas de información, como el modelo de DeLone y McLean, se diseñó una encuesta con 16 preguntas cerradas por medio de una escala Likert de 5 puntos y una pregunta abierta. Se obtuvieron 309 respuestas de estudiantes y se utilizó una metodología de análisis mixto a partir de un modelo de ecuaciones estructurales (SEM) para interpretar los datos cuantitativos y el análisis de contenido como método cualitativo. RESULTADOS. La evaluación resalta la importancia de los factores que inciden directamente en la percepción del estudiante sobre la calidad del servicio prestado y la calidad de la información proporcionada. El análisis cualitativo reveló la importancia que los estudiantes otorgan al contenido práctico. DISCUSIÓN. Estos resultados corroboran las conclusiones de gran parte de los trabajos anteriores en los que se determina la necesidad de desarrollar y difundir modelos metodológicos más activos basados en la práctica y el desarrollo profesional. Además, es necesario reforzar la eficacia y la calidad de la información, proporcionando recursos adicionales y ofreciendo actividades prácticas en las que se expongan ejemplos de aplicación de los contenidos.

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“…With respect to creating content, issues of low motivation can prevent learners from properly engaging with the generative aspects of learnersourcing. Indeed, prior research has shown that students tend to be much more inclined to use and evaluate resources created by others than they are to create resources themselves [9,10,11]. With respect to utilising the resources produced by other learners, one of the widely cited issues with learnersourcing is the low quality of some of the content generated by novices [12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing

Denny¹,

Sarsa²,

Hellas³

et al. 2022

Preprint

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In this article, we introduce and evaluate the concept of robosourcing for creating educational content. Robosourcing lies in the intersection of crowdsourcing and large language models, where requests to large language models replace some of the work traditionally performed by the crowd. Robosourcing includes a human-in-the-loop to provide priming (input) as well as to evaluate and potentially adjust the generated artefacts; these evaluations could also be used to improve the large language models. We explore the feasibility of robosourcing in the context of education by conducting an evaluation of robosourced programming exercises, generated using OpenAI Codex. Our results suggest that robosourcing could significantly reduce human effort in creating diverse educational content while maintaining quality similar to human-created content. Thus, we argue that robosourcing has the potential to alleviate known issues around learner motivation and content quality that have been shown to limit the benefits of learnersourcing in practice.

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What's In It for the Learners? Evidence from a Randomized Field Experiment on Learnersourcing Questions in a MOOC

Cited by 15 publications

References 28 publications

Lessons Learned From Four Computing Education Crowdsourcing Systems

Lessons Learned From Four Computing Education Crowdsourcing Systems

Criterios de calidad de un MOOC basado en la valoración de los estudiantes

Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing

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