Identifying Profiles of Collaborative Problem Solvers in an Online Electronics Environment (original) (raw)
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Are You Really a Team Player?: Profiles of collaborative problem solvers in an online environment
2020
Collaborative problem solving (CPS) is considered a necessary skill for students and workers in the 21 century as the advent of technology requires more and more people to frequently work in teams. In the current study, we employed theoretically-grounded data mining techniques to identify four profiles of collaborative problem solvers interacting with an online electronics task. The profiles were created based on 11 theoretically-grounded CPS skills defined a priori. The resulting four profiles correlated in expected directions with in-task performance and had interesting relationships with external measures associated with prior knowledge and CPS skills. These results inform and partially replicate findings from our previous research using a similar approach on a smaller dataset. Implications and comparisons between the two studies will be discussed.
Are You Really a Team Player? Profiling of Collaborative Problem Solvers in an Online Environment
Educational Data Mining, 2020
Collaborative problem solving (CPS) is considered a necessary skill for students and workers in the 21 st century as the advent of technology requires more and more people to frequently work in teams. In the current study, we employed theoretically-grounded data mining techniques to identify four profiles of collaborative problem solvers interacting with an online electronics task. The profiles were created based on 11 theoretically-grounded CPS skills defined a priori. The resulting four profiles correlated in expected directions with in-task performance and had interesting relationships with external measures associated with prior knowledge and CPS skills. These results inform and partially replicate findings from our previous research using a similar approach on a smaller dataset. Implications and comparisons between the two studies will be discussed.
Journal of Computer Assisted Learning, 2022
Objective: We explore possibilities for automated annotation of actions in collaborative-teams, especially chat messages. We evaluate two approaches that employ machine learning for automated classification of CPS events. Method: Data were collected from engineering, physics and electronics students' participation in a simulation-based task on electronics concepts, in which participants communicated via text-chat messages. All task activities were logged and time stamped. Data have been manually classified for the CPS skills, using an ontology that includes both social and cognitive dimensions. In this article, we describe computational linguistic methods for automatically classifying the CPS skills from logged data, with a view towards automating CPS assessments. Results: We applied two machine learning methods to our data. A Naïve Bayes classifier has been previously used in CPS research, but it is only moderately successful on our data. We also present a k-nearest-neighbours (kNN) classifier that uses distributional semantic models for measuring text similarity. This classifier shows strong agreement between automated and human annotations. The study also demonstrates that automatic spelling correction and slang normalization of chat texts are useful for accurate automated annotation.
2012
Abstract. Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collaboration. We have developed a scalable data mining method, whose objective is to infer information on the collaboration during the collaboration process in a domain-independent way and to improve collaboration process management and learning in an open collaborative educational web environment. Thus, we used statistical indicators of learner’s interactions in forums as the data source and a clustering algorithm to classify the data according to learner’s collaboration. We showed the information on learner’s collaboration to the tutor and learners to help them with collaboration process management. The experimental results support this method. 1
Journal of Intelligence
Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals’ CPS competency are necessary, as traditional assessment types such as multiple -choice items are not well suited for such a process-oriented competency. In a move to computer-based environments to support CPS assessment, innovative computational approaches are also needed to understand individuals’ CPS behaviors. In the current study, we describe the use of a simulation-based task on electronics concepts as an environment for higher education students to display evidence of their CPS competency. We further describe computational linguistic methods for automatically characterizing students’ display of various CPS skills in the task. Comparisons between such an automated approach and an approach based on human annotation to characterize student CPS behaviors revealed above average agr...
Computers in Human Behavior, 2018
Collaborative problem solving (CPS) is a complex construct comprised of skills associated with social and cognitive dimensions. The diverse set of skills within these dimensions make CPS difficult to measure. Typically, research on measuring CPS has used highly constrained environments that help narrow the problem space. In the current study, we applied the in-task assessment framework to support the exploration of CPS skills at a deep level in an open digital environment in which three students worked together to solve an electronics problem. The construct of CPS was defined in depth prior to the implementation of the environment through the development of a complex, hierarchical ontology. The features from the ontology were identified in the data and four theoretically-grounded profiles of types of collaborative problemsolvers were produced-high social/high cognitive, high social/low cognitive, low social/high cognitive, and low social/low cognitive. Results showed that students in the low social/low cognitive profile group demonstrated poorer performance than students in other profile groups. Further, having at least one high social/high cognitive member in a team facilitated performance. This study offers groundwork for future studies in measuring CPS with an approach suitable for less constrained collaborative environments.
Journal of Information Technology Research, 2019
Nowadays it is quite common for universities to use computer-supported collaborative learning (CSCL) systems to favor group learning and teaching processes. CSCL systems provide communication, coordination and collaboration tools that ease group dynamic regardless space-time location of group members. However, forming a group and having the technology to support group tasks is not enough to guarantee students collaboration. Effective collaboration supposes the manifestation of specific roles by group members. Considering that group roles are conditioned (among other factors) by collaborative skills that students manifest, this article explores relations between collaborative skills and group roles by means of the application of association rules over a dataset of university students' interactions during CSCL sessions. The discovered knowledge might be used for automatic recognition of student roles based on collaborative skills that students manifest in their groups. Furthermore...
Method and Tools for analysis of collaborative problem-solving activities
2004
This paper provides an overview of the “Object-oriented Collaboration Analysis Framework (OCAF)” a method proposed for analysis and evaluation of collaborative problem solving activities of groups of actors, mediated by collaboration-support technology. This framework puts emphasis on the abstract and tangible objects that appear during the development of a solution to a given problem. The notions of the “objects' histories” and “objects' ownership” are introduced by this analytical framework. In the paper tools that ...
2009
Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collaboration. We have developed a scalable data mining method, whose objective is to infer information on the collaboration during the collaboration process in a domain-independent way and to improve collaboration process management and learning in an open collaborative educational web environment. Thus, we used statistical indicators of learner's interactions in forums as the data source and a clustering algorithm to classify the data according to learner's collaboration. We showed the information on learner's collaboration to the tutor and learners to help them with collaboration process management. The experimental results support this method.
Analyzing groups’ problem-solving process to characterize collaboration within groups
2018
Collaborative learning has gained much research attention in the past few years given the cognitive benefits attributed to it. We are investigating automatic adaptive support to groups in a computer supported collaborative learning (CSCL) system. In this paper, we report a study in which we observed the joint-problem solving processes of a teams of three learners. We adopted a Sudoku puzzle as our learning task. We analyze data collected from groups' problem-solving to identify different states of individuals' participative activities within groups. We also determine indicators, activators and inhibitors of collaboration during joint problem-solving (JPS) to characterize group learning activities. Our findings together with the related work provide a foundation for further studies that will design an appropriate technological solution for a sharedactivity group environment. This environment will be enhanced to gather collaboration data, evaluate the level of group interaction, determine the need and kind of support to groups and finally, provide real-time adaptive support to learning groups for enhanced collaboration.