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Fine-Grained Recording of Student Programming Sessions to Improve Teaching and Time Estimations
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).ORCID-id: 0000-0001-5335-5196
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).ORCID-id: 0000-0003-1154-5308
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV). (DISA ; DSIQ)ORCID-id: 0000-0003-1173-5187
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).ORCID-id: 0000-0002-0835-823X
2016 (Engelska)Ingår i: International journal of engineering education, ISSN 0949-149X, Vol. 32, nr 3, s. 1069-1077Artikel i tidskrift (Refereegranskat) Published
Resurstyp
Text
Abstract [en]

It is not possible to directly observe how students work in an online programming course. This makes it harder for teachers to help struggling students. By using an online programming environment, we have the opportunity to record what the students actually do to solve an assignment. These recordings can be analyzed to provide teachers with valuable information. We developed such an online programming tool with fine-grained event logging and used it to observe how our students solve problems. Our tool provides descriptive statistics and accurate replays of a student's programming sessions, including mouse movements. We used the tool in a course and collected 1028 detailed recordings. In this article, we compare fine-grained logging to existing coarse-grained logging solutions to estimate assignment-solving time. We find that time aggregations are improved by including time for active reading and navigation, both enabled by the increased granularity. We also divide the time users spent into editing (on average 14.8%), active use (on average 37.8%), passive use (on average 29.0%), and estimate time used for breaks (on average 18.2%). There is a correlation between assignment solving time for students who pass assignments early and students that pass later but also a case where the times differ significantly. Our tool can help improve computer engineering education by providing insights into how students solve programming assignments and thus enable teachers to target their teaching and/or improve instructions and assignments.

Ort, förlag, år, upplaga, sidor
Chicago, IL: Tempus Publications , 2016. Vol. 32, nr 3, s. 1069-1077
Nyckelord [en]
computer science education, learning analytics, educational data mining, computer engineering education
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
URN: urn:nbn:se:lnu:diva-55062ISI: 000378700600003Scopus ID: 2-s2.0-84973569061OAI: oai:DiVA.org:lnu-55062DiVA, id: diva2:949940
Konferens
20th Annual Conference on Innovation and Technology in Computer Science Education, JUL 06-08, 2015, Vilnius, LITHUANIA
Tillgänglig från: 2016-07-26 Skapad: 2016-07-22 Senast uppdaterad: 2019-08-13Bibliografiskt granskad

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Toll, DanielOlsson, TobiasEricsson, MorganWingkvist, Anna

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