Pregled bibliografske jedinice broj: 1153185
Using Text Mining to Extract Information from Students' Lab Assignments
Using Text Mining to Extract Information from Students' Lab Assignments // Proceedings of MIPRO 2021 44th International Convention / Skala, Karolj (ur.).
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2021. str. 643-646 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1153185 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Using Text Mining to Extract Information from
Students' Lab Assignments
Autori
Gusić, Jelena ; Šimić, Diana
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of MIPRO 2021 44th International Convention
/ Skala, Karolj - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2021, 643-646
Skup
44th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2021)
Mjesto i datum
Opatija, Hrvatska, 27.09.2021. - 01.10.2021
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Text mining ; Hierarchical cluster analysis ; Learning analytics ; Assignments evaluation
Sažetak
We use text mining as a part of formative assessment in one generation of the first year undergraduate students enrolled in our Statistics course. Practical lab exercises were done in R and RStudio. During the semester, students wrote weekly assignments. There were 11 assignments. Assignments were pre pared in Rmarkdown, and students added necessary code and text to the Rmarkdown document, knitted the document and submitted the generated html files. We wanted to identify students whose submissions were not substantially different from the assignment (substandard work) and groups of students who submitted similar texts (possible collusion or plagiarism). Additionally, we wanted to compare results of text mining Text analyses were done in R using packages XML, RCurl, RWeka and tm. A document term matrix was created with trigrams. For each assignment, distance between submitted texts was calculated as the Euclidian distance. Hierarchical cluster analysis with complete linkage was used to identify similarities between students' assignments. The initial assignment was included for comparison. Clusters of assignments with very small inter assignment distances pointed to similarities of assignments. Students whose work was most distant from others were found to be either excellent, or failing students.
Izvorni jezik
Engleski
Znanstvena područja
Informacijske i komunikacijske znanosti
POVEZANOST RADA
Projekti:
HRZZ-IP-2020-02-5071 - Podizanje zrelosti visokih učilišta za implementaciju analitika učenja (HELA) (Begičević Ređep, Nina, HRZZ - 2020-02) ( CroRIS)
Ustanove:
Fakultet organizacije i informatike, Varaždin