Crowdsourcing in Computer Science Education

Pirttinen, Nea

doi:10.1145/3446871.3469781

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…Future crowdsourcing exercises could build on this methodology to extend th ticipatory nature of the project through gamification [3,20] and/or incorporate r technologies. Students could be given the anonymous opportunity to evaluate da lected by other students to improve overall data quality [2], which could also be u…”

Section: Discussionmentioning

confidence: 99%

“…Future crowdsourcing exercises could build on this methodology to extend the participatory nature of the project through gamification [3,20] and/or incorporate related technologies. Students could be given the anonymous opportunity to evaluate data collected by other students to improve overall data quality [2], which could also be used to create reputational rewards [6,9]. Projects designed to support a social cause may be well received by students in some situations [6].…”

Section: Discussionmentioning

confidence: 99%

“…Crowdsourcing can develop field data to support environmental monitoring (as with this study), can create educational content [2], and can complete existing tasks through commercial crowdsourcing platforms, which could include digital content creation [4] or content recognition [10]. One of the important benefits of an educational crowdsourcing assignment is that by involving students in the crowdsourcing process, it is possible to teach the meaning and value of the crowdsourcing technique.…”

Section: Crowdsourcing Initiatormentioning

confidence: 99%

“…and skills (e.g., prior knowledge of crowdsourcing) can be determined prior to the crowdsourcing event by conducting a survey using freely available web-based tools (e.g., Google Forms). Differences in the skills and abilities of students have been used to both support other students through peer reviews [2] and to develop course content [2].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Teaching Field Data Crowdsourcing Using a GPS-Enabled Cellphone Application: Soil Erosion by Water as a Case Study

et al. 2022

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Crowdsourcing is an important tool for collecting spatio-temporal data, which has various applications in education. The objectives of this study were to develop and test a laboratory exercise on soil erosion by water and field data crowdsourcing in an online introductory soil science course (FNR 2040: Soil Information Systems) at Clemson University. Students from different STEM disciplines (wildlife biology, forestry, and environmental and natural resources) participated in the study in the fall of 2021. They completed a sequence of self-contained digital teaching modules or reusable learning objects (RLOs), which are often used in online learning. The exercise included a field exercise and learning module to teach students about different types of water-based soil erosion as well as field data collection and crowdsourcing tools. As a result of this exercise, student familiarity with crowdsourcing was effectively increased, as shown by the post-assessment survey with a +31.2% increase in the “moderately familiar” category and a +28.3% increase in the “extremely familiar” category. The online quiz contained ten questions and was taken by 56 students with an average score of 9.5 (out of 10). A post-assessment survey found that most of the students indicated that the laboratory was an effective learning experience about field data crowdsourcing using a GPS-enabled cellphone application. Detailed students’ comments indicated enjoyment of learning (e.g., data collection, learning about different technologies), the value of multimedia (e.g., ArcGIS Survey123, cellphone), the flexibility of learning (e.g., field work), the content applicability (e.g., actual field examples of erosion by water), and criticism (e.g., technical issues). A word cloud derived from students’ comments about their laboratory exercise experience indicated the most frequent words used by students, such as “erosion”, “enjoyed”, and “different”, among others. Incorporating a learning module and field exercise using modern data collection technology into an undergraduate soil science education course enabled students to understand the value and methods for leveraging cellphone-based field collection methods to crowdsource data for environmental assessment. Practical recommendations for planning and executing future crowdsourcing exercises were developed using the current study as an example.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Crowdsourcing Initiatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Teaching Field Data Crowdsourcing Using a GPS-Enabled Cellphone Application: Soil Erosion by Water as a Case Study

et al. 2022

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“…At the same time, students can use appropriate tools and techniques to create course assignments. The author in [22] has examined the effects of crowdsourcing in computer education systems on students' learning and the different quality-based assurance methods necessary to utilize the products students create using these technologies. The decomposable tasks submitted to the crowdsourcing environment are challenging to solve in the decomposition and composition way due to the difficulties of selecting appropriate workers.…”

Section: Related Workmentioning

confidence: 99%

CTWS: A Crowd-Powered Framework for Scheduling Decomposable Complex-Tasks

Kumar,

Pandey

2024

Preprint

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A crowdsourcing framework involves a pool of workers competing to solve a distributed task posted by a requester on a crowdsourcing platform. Each worker divides a decomposable task (i.e., a complex distributed task) into multiple subtasks, considering their uniqueness, and shares such decomposition with the platform. It is challenging for a requester to map an optimal worker set with a decomposable task, ensuring a cost-effective solution. We present the Complex-Task Worker-Set (CTWS) method to address this challenge. The algorithm optimizes the solution cost of a decomposable task and reduces the computation time of selecting different workers. This paper discusses a model integrated with CTWS of a crowdsourcing environment where workers and requester(s) work together through the crowdsourcing platform. We use an aging technique, which plays a crucial role in tie-break when an ambiguity arises to select the best path among a set of solution paths. Thereafter, the optimized solution is provided to the requesters based on the requester's constraints and requirements. The results confirm the effectiveness of the CTWS against the state-of-the-art.

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