Hans Spada

Learning to Collaborate: An Instructional Approach to Promoting Collaborative Problem Solving in Computer-Mediated Settings

Effective collaboration in computer-mediated settings among spatially distributed people is a precondition for success in many new learning and working contexts but it is hard to achieve. We have developed two instructional approaches to improve collaboration in such settings by promoting peoples capabilities to collaborate in a fruitful way and furthering their understanding of what characterizes good collaboration. The rationale is that strategies necessary for a good and effective computer-mediated collaboration may be conveyed to people by exposing them to an elaborated worked-out collaboration example (observational learning) or by giving them the opportunity to learn from scripted collaborative problem-solving. An experimental study was conducted that compared learning from observing a worked-out collaboration example with the learning effects of scripted collaborative problem-solving, the effects of unscripted collaborative problem-solving, and a control condition without a learning phase. The experimental design provided clearly separated phases for the instructional treatments (learning phase) and for applying and testing the acquired skills (application phase). Both observing a worked-out collaboration example and collaborating with a script during the learning phase showed positive effects on process and outcome of the second collaboration in the application phase.

A rating scheme for assessing the quality of computer-supported collaboration processes

The analysis of the process of collaboration is a central topic in current CSCL research. However, defining process characteristics relevant for collaboration quality and developing instruments capable of assessing these characteristics are no trivial tasks. In the assessment method presented in this paper, nine qualitatively defined dimensions of collaboration are rated quantitatively: sustaining mutual understanding, dialogue management, information pooling, reaching consensus, task division, time management, technical coordination, reciprocal interaction, and individual task orientation. The data basis for the development of these dimensions was taken from a study in which students of psychology and medicine collaborated on a complex patient case via a desktop-videoconferencing system. A qualitative content analysis was performed on a sample of transcribed collaboration dialogue. The insights from this analysis were then integrated with theoretical considerations about the roles of communication, joint information processing, coordination, interpersonal relationship, and motivation in the collaboration process. The resulting rating scheme was applied to process data from a new sample of 40 collaborating dyads. Based on positive findings on inter-rater reliability, consistency, and validity from this evaluation, we argue that the new method can be recommended for use in different areas of CSCL.

Evaluation of environmental problems: A coherence model of cognition and emotion

This article presents a computational framework for understanding how media information about environmental problems influences cognition, emotion, and behaviour. The theoretical assumptions are formally specified and implemented in the computer model ITERA (Intuitive Thinking in Environmental Risk Appraisal) using a constraint satisfaction network. The model makes predictions about the cognitive evaluation of environmental problems, about the development of distinct emotions (anger and sadness), and about the resulting action tendencies. In addition, the model describes how cognitions and emotions interact in making judgements entailing coherence biases. In three experiments (N = 258), we presented manipulated media reports about environmental damages. The effects of three variables (knowledge about the riskiness of an action; higher goal of the actor; voluntariness of the actor) were compared with the models predictions. The empirical data confirmed the predicted coherence effects for the cognitive appraisal. Likewise, the models predictions for anger corresponded well with the empirical results. Assumptions concerning sadness, however, were only partially confirmed.

Learning to collaborate while being scripted or by observing a model

In an earlier study, we had tested if observing a collaboration model, or alternatively, following a collaboration script could improve students’ subsequent collaboration in a computer-mediated setting and promote their knowledge of good collaboration. Both model and script showed positive effects. The current study was designed to further probe the effects of model and script by comparing them to conditions in which the learning was supported by providing elaboration support (instructional prompts and a reflective self-explanation phase). In addition, we applied a newly developed, innovative rating scheme to analyze the collaborative process: The rating scheme combines qualitative evaluation with quantitative assessment. Forty dyads were tested, eight in each of the following conditions: model plus elaboration, model, script plus elaboration, script, and control. Observing a collaboration model with elaboration support yielded the best results over all other conditions on measures of the quality of collaborative process and on outcome variables. Model without elaboration was second best. The results for the script conditions were mixed; on some variables, even below those of the control condition. The results of the current study lead us to challenge the positive view on collaboration scripts prevalent in CSCL research. We propose adaptive scripting as a possible solution.

Are two heads always better than one? Differential effects of collaboration on students’ computer-supported learning in mathematics

While some studies found positive effects of collaboration on student learning in mathematics, others found none or even negative effects. This study evaluates whether the varying impact of collaboration can be explained by differences in the type of knowledge that is promoted by the instruction. If the instructional material requires students to reason with mathematical concepts, collaboration may increase students’ learning outcome as it promotes mutual elaboration. If, however, the instructional material is focused on practicing procedures, collaboration may result in task distribution and thus reduce practice opportunities necessary for procedural skill fluency. To evaluate differential influences of collaboration, we compared four conditions: individual vs. collaborative learning with conceptual instructional material, and individual vs. collaborative learning with procedural instructional material. The instruction was computer-supported and provided adaptive feedback. We analyzed the effect of the conditions on several levels: Logfiles of students’ problem-solving actions and video-recordings enabled a detailed analysis of performance and learning processes during instruction. In addition, a post-test assessed individual knowledge acquisition. We found that collaboration improved performance during the learning phase in both the conceptual and the procedural condition; however, conceptual and procedural material had a differential effect on the quality of student collaboration: Conceptual material promoted mutual elaboration; procedural material promoted task distribution and ineffective learning behaviors. Consequently, collaboration positively influenced conceptual knowledge acquisition, while no positive effect on procedural knowledge acquisition was found. We discuss limitations of our study, address methodological implications, and suggest practical implications for the school context.

A new method to assess the quality of collaborative process in CSCL

In CSCL research, the collaborative process - the way people collaborate while working on tasks and learning -- is of central importance. Instructional measures are being developed to improve the quality of the collaboration which itself determines to a great extent the results of working and learning in groups. However, assessing collaborative process is not easy. We have developed a new assessment method by quantitatively rating nine qualitatively defined characteristic dimensions of collaboration. In this paper, we first describe how these dimensions were extracted from video-recordings of dyads collaborating to solve interdisciplinary tasks. Then we explain how the resulting rating system was applied to and tested on another sample. Based on positive findings from this application, we argue that the new method can be recommended for different areas of CSCL research.

Can People Learn Computer-Mediated Collaboration by Following A Script?

Our central hypothesis is that partners who jointly work on a task in a computer-mediated setting following a collaboration script, can acquire collaborative skills that will help to improve the collaboration in subsequent tasks as well as their outcome. In an experimental study, a collaboration script was provided for a first computer-mediated collaboration in one experimental condition. Meantime, in a different experimental condition, the collaborators observed a model-collaboration. Learning effects of script and model were expected to become evident in the process and outcome of a second, unscripted computer-mediated collaboration. Compared to two control conditions (a condition with unsupported collaboration during the learning phase and a condition without a learning phase) both the script condition and the model condition showed positive effects on process and outcome during the application phase. This leads to the conclusion that collaboration scripts can indeed constitute a promising instructional method to promote collaborative competences and to improve subsequent computer-mediated collaboration.

The logic of content effects in propositional reasoning: The case of conditional reasoning with a point of view

In order to resolve the controversial discussion regarding content effects in deductive reasoning, we propose distinguishing between two inferential sources—an arguments form, and additional relations people associate with the arguments content—and analysing their interplay. Both sources are equally necessary in order to understand the role content plays in deductive reasoning. People make valid deductions from the content relations (content competence), but in thematic reasoning tasks, these deductions lead to the intriguing phenomenon known as content effects. Focusing on the interplay of both sources of inferences, the dual source distinction enables a novel class of predictions to be made, namely the correct mastery of the logical connectors (form competence) in tasks that require the individual to think about an arguments form in relation to its content. To illustrate the dual source approach and its implications, the selection task paradigm of conditional reasoning with a point of view is used in combination with two content domains: conditional promises with cheating and non-cheating perspectives and technical systems with causal perspectives. Experimental findings corroborate all three phenomena: content competence, content effects, and form competence. The dual source distinction is discussed with regard to current theories of reasoning.

Learning Qualitative and Quantitative Reasoning in a Microworld for Elastic Impacts.

From a psychological point of view efficient teaching by means of an intelligent tutoring system necessarily involves that the communication of knowledge is adapted to the requirements of the learner: to her cognitive abilities, her pre-instructional knowledge and her learning capabilities. To tackle these topics in a precise way, we have developed the artificial-intelligence-based microworld DiBi (disk billiard) and MULEDS, a multi-level diagnosis system. The microworld DiBi sets up a learning environment which simulates elastic impacts as a subtopic of classical mechanics. DiBi enables and supports reasoning on different levels of mental domain representation ordered along the dimension ‘qualitative/quantitative’. This way of representing the domain provides a basis for passive adaptation in an advanced way. Correspondingly, active adaptation is supported by MULEDS, wherein student modeling is realized by assessing the student’s correct and/or incorrect domain-specific knowledge at these different levels. Within this psychological perspective, the use of instructional tools, such as the microworld DiBi and the computerized diagnosis system MULEDS, aims at gradually supporting and guiding the student in the construction of more and more powerful an sound domain representations. The progression through these levels of domain representation will enable the student to solve the problems posed by the domain in a flexible way.RésuméD’un point de vue psychologique, un enseignement efficace avec un tutoriel intelligent implique nécessairement que la connaissance à communiquer soit adaptée aux besoins de l’élève: ses aptitudes cognitives, ses connaissances préalables et sa capacité d’apprentissage. Pour aborder ces problèmes, nous avons développé le système DiBi (disk billiard) — un micro-monde basé sur l’Intelligence Artificielle — et MULEDS (multi-level diagnosis system), un système de diagnostic à plusieurs niveaux. Le micro-monde DiBi présente un environnement d’apprentissage simulant des chocs élastiques en mécanique classique. DiBi facilite le raisonnement à plusieurs niveaux de représentation mentale caractérisés selon la dimension «qualitatif/quantitatif». Le système MULEDS assure l’adaptation de l’environnement aux réponses de l’élève. Celui-ci est modélisé en fonction des connaissances spécifiques — correctes ou incorrectes — correspondant aux différents niveaux. L’utilisation d’outils d’enseignements comme DiBi ou MULEDS vise à soutenir et à guider l’élève, dans la construction de représentations de plus en plus élaborées et puissantes. La progression à travers les différents niveaux de représentation devrait permettre d’accroître la flexibilité dans la résolution de problèmes.

Conceptual change or multiple representations

Abstract Several of the contributions to this issue take a common position in central questions. Conceptual change is discussed as a process, which leads from one coherent mental structure to another, which represents the world more adequately. Radical deep rooted transformations are hypothesized. In the commentary this view is compared with competing notions of knowledge completion and revision, the knowledge in pieces hypothesis, the multiple mental representations theory, etc. The necessity to develop formalized simulation models of the process of conceptual change is emphasized. Relevant work in the field of AI on belief revision is addressed.