On the Discovery of Educational Patterns using Biclustering

Henriques, Rui; Finamore, Anna Carolina; Casanova, Marco A.

doi:10.1007/978-3-030-22244-4_17

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We collected two datasets of students' performance: ADS data (Henriques, Finamore, and Casanova 2019) and MATH data. The ADS dataset is provided by the Department of Informatics of the Pontifical Cathaolic University of Rio de Janero (PUC-Rio), which captures the performance of students along the topics of the Advanced Data Structures (ADS) course.…”

Section: Datasets and Preprocessingmentioning

confidence: 99%

“…To date, serveral studies have been published to cluster students into meaningful groups based on their characteristics with a goal of driving group instructions (Amershi and CONATI 2009;Dutt, Ismail, and Herawan 2017;Mojarad et al 2018;Henriques, Finamore, and Casanova 2019). These methods can be mainly divided into two major categories: traditional clustering methods and biclustering methods.…”

Section: Introductionmentioning

confidence: 99%

“…Mean-shift clustering is used to select a number of clusters, and then k-mean clustering is applied to identify distinct student profiles. (Henriques, Finamore, and Casanova 2019) is one recent application of biclustering method in educational data. The authors use patternbased biclustering approach to detect non-trivial, yet potentially relevant educational and statistically significant patterns from the performance of students' data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring Common and Individual Characteristics of Students via Matrix Recovering

Wang,

Teng,

Zhou

et al. 2020

Preprint

View full text Add to dashboard Cite

Balancing group teaching and individual mentoring is an important issue in education area. The nature behind this issue is to explore common characteristics shared by multiple students and individual characteristics for each student. Biclustering methods have been proved successful for detecting meaningful patterns with the goal of driving group instructions based on students' characteristics. However, these methods ignore the individual characteristics of students as they only focus on common characteristics of students. In this article, we propose a framework to detect both group characteristics and individual characteristics of students simultaneously. We assume that the characteristics matrix of students' is composed of two parts: one is a low-rank matrix representing the common characteristics of students; the other is a sparse matrix representing individual characteristics of students. Thus, we treat the balancing issue as a matrix recovering problem. The experiment results show the effectiveness of our method. Firstly, it can detect meaningful biclusters that are comparable with the state-of-the-art biclutering algorithms. Secondly, it can identify individual characteristics for each student simultaneously. Both the source code of our algorithm and the real datasets are available upon request.

show abstract

Section: Datasets and Preprocessingmentioning

confidence: 99%