Improving MOOC student learning through enhanced peer-to-peer tasks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 10254 LNCS, Page: 140-149
2017
- 3Citations
- 21Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
In the context of MOOCs, activities that imply a deeper learning are, undoubtedly, P2P tasks. However, the traditional MOOC structure makes very difficult to evaluate the learning level obtained by students when performing these activities. This situation is especially problematic as students increasingly demand the universities to certify the knowledge acquired by means of MOOCs, so higher education institutions must guarantee their learning. In order to address this challenge, in this paper it is proposed a new type of P2P activity, designed to automatically provide students a valuable feedback about their work. This new type of activity is supported by a module, including a coordination engine and a formal revision component communicating by means of the LTI protocol with an automatic revision assistant, which allows differentiating a genuine contribution from the simple repetition of ideas. Moreover, and experimental validation is carried out. Results show first evidences that most students (up to 82%) improve their learning when employed the new proposed technology.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85019709125&origin=inward; http://dx.doi.org/10.1007/978-3-319-59044-8_16; http://link.springer.com/10.1007/978-3-319-59044-8_16; http://link.springer.com/content/pdf/10.1007/978-3-319-59044-8_16; https://dx.doi.org/10.1007/978-3-319-59044-8_16; https://link.springer.com/chapter/10.1007/978-3-319-59044-8_16
Springer Nature
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know