Forming Groups for Collaborative Learning of Introductory Computer Programming Based on Students’ Programming Skills and Learning Styles

Tobar, Carlos Miguel; Adán-Coello, Juan Manuel; Faria, Eustáquio São José de; Menezes, Wiris Serafim de; Freitas, Ricardo Luís de

doi:10.4018/jicte.2011100104

Cited by 17 publications

(4 citation statements)

References 16 publications

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“…For example, the at times complementary topic-master or skills may be helpful [14,16]. Another approach has been to create groups of students who approach problems very differently [1]. This system also grouped students with complementary learning styles to avoid conflicts stemming from incompatible learning types.…”

Section: Group Compositionmentioning

confidence: 99%

“…We also asked students how important the course was to them, what other courses they are currently taking, and their preferred discussion section attendance times (to allow students to attend discussion section together if possible). 1 We obtained student consent and IRB approval of our human research protocol titled Creating positive experiences for first-year underrepresented students through scalable automated inclusive study group formation., with Protocol ID '2020-08-13526' and Approval Date 'September 25, 2020. ' Our aim was to create a structured process for supporting and creating informal study groups, all while ensuring inclusion of students from underrepresented groups.…”

Section: Collecting Student Preferencesmentioning

confidence: 99%

See 1 more Smart Citation

Inclusive Study Group Formation At Scale

Kohli¹,

Ramachandran²,

Tudor³

et al. 2022

Preprint

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The student peer-group is one of the most important influences on student development. Group work is essential for creating positive learning experiences, especially in remote-learning where student interactions are more challenging. While the benefits of study groups are established, students from underrepresented communities often face challenges in finding social support for their education when compared with those from majority groups. We present a system for flexible and inclusive study group formation that can scale to thousands of students.Our focus is on long-term study groups that persist throughout the semester and beyond. Students are periodically provided opportunities to obtain a new study group if they feel their current group is not a good fit. In contrast to prior work that generates single-use groups, our process enables continuous refinement of groups for each student, which in turn informs our algorithm for future iterations.We trialed our approach in a 1000+ student introductory Electrical Engineering and Computer Science course that was conducted entirely online during the COVID-19 pandemic. We found that of all students matched to study groups through our algorithm, a large majority felt comfortable asking questions (78%) and sharing ideas (74%) with their group. Students from underrepresented backgrounds were more likely to request software-matching for study groups when compared with students from majority groups. However, underrepresented students that we did match into study groups had group experiences that were just as successful as students from' majority groups. Students in engaged, regularly participating study groups had more positive results across all other indicators of the study group experience, and certain positive group experiences were associated with higher exam scores overall. Furthermore, students performing at a B-level on the first class midterm, who participated in high-quality software-matched study groups, demonstrated higher final exam scores than students in lowerquality groups.

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Section: Group Compositionmentioning

confidence: 99%

Section: Collecting Student Preferencesmentioning

confidence: 99%

Inclusive Study Group Formation At Scale

Kohli¹,

Ramachandran²,

Tudor³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…34 Adán-Coello et al, included learning styles for forming groups for collaborative learning of introductory computer programming. 35 Learning styles are also included for the implementation of the adaptive system in electronic learning. 36 Tang and Mccalla proposed a clustering algorithm based on large generalized sequences to find groups of students with similar learning characteristics based on their traversal path patterns and the content of each page they have visited.…”

Section: Published By Atlantis Pressmentioning

confidence: 99%

Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study

Jovanović¹,

Vukičević²,

Milovanović³

et al. 2012

IJCIS

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In this research we applied classification models for prediction of students' performance, and cluster models for grouping students based on their cognitive styles in e-learning environment. Classification models described in this paper should help: teachers, students and business people, for early engaging with students who are likely to become excellent on a selected topic. Clustering students based on cognitive styles and their overall performance should enable better adaption of the learning materials with respect to their learning styles. The approach is tested using well-established data mining algorithms, and evaluated by several evaluation measures. Model building process included data preprocessing, parameter optimization and attribute selection steps, which enhanced the overall performance. Additionally we propose a Moodle module that allows automatic extraction of data needed for educational data mining analysis and deploys models developed in this study.

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“…Some may absorb skills from actionable steps from massive open online courses (MOOCs) such as Coursera ( https://www.coursera.org/ ), Udemy ( https://www.udemy.com/ ) and such, or a quick primer from DataCamp ( https://www.datacamp.com/ ) or Software Carpentry ( https://software-carpentry.org/ ). Hands-on workshops on software or coding languages from your institution or elsewhere can provide opportunities not only for immersive learning [ 3 ] but also for collaborative learning [ 4 ]. Of course, in the beginning, you may feel intense fear of missing out (FOMO) regarding every workshop in your vicinity.…”

Section: Introductionmentioning

confidence: 99%