Assessing and promoting computer-supported collaborative learning

Assessing and promoting computer-supported collaborative learning Anne Meier University of Freiburg, Institute of Psychology [email_address]

Introduction to CSCL (computer-supported collaborative learning)

Assessing CSCL learning processes

Supporting CSCL learning processes

Example study: adaptive support for knowledge co-construction

Overview

The CSCL community

a short history of CSCL…

“ seeds” in the 1980s, e.g. 1989 NATO-sponsored workshop “computer-supported collaborative learning” (Maratea, Italy)

since 1995: bi-annual CSCL conferences

since 2003: CSCL community part of International Society of the Learning Sciences (ISLS)

own journal: International Journal of CSCL (ijCSCL) published by Springer since 2006

highly interdisciplinary community

Introduction to CSCL

CSCL researchers study:

How people can learn together with the help of computers (Stahl, Koschmann, & Suthers, 2007)

How technology can facilitate the sharing and creation of knowledge and expertise through peer interaction and group learning processes (Restra & Laferrière, 2007)

advantages/strengths

challenges/pitfalls

What is your experience with (computer-supported) collaborative learning?

Neo-Piagetian perspective

learning = cognitive restructuring

resolving socio-cognitive conflict arising from peer collaboration

Cognitive elaboration perspective

learning = elaboration and integration of knowledge

very important: constructing explanations

Neo-Vygotskian perspective

learning = appropriation, internalization

knowledge co-construction; scaffolding and fading

Situated learning perspective

learning = increasingly “central” participation in a community of practice

distributed cognition: persons, tools, symbols, artefacts,…

What makes collaborative learning effective? See for example: Cohen, 1994; Dillenbourg et al., 1995; Fischer, 2002; Webb & Palincsar, 1996)

Motivational process loss (e.g. Salomon & Globerson, 1989)

Free-rider effect (“social loafing”)

Sucker effect

Production blocking

having to wait for others to finish their turn

e.g. in brainstorming (Diehl & Stroebe, 1987)

Biased information sampling (e.g. Brodbeck et al., 2007; Stasser & Titus, 1985))

neglecting individuals’ unique knowledge

striving for consensus rather than understanding

 Putting people in a (computer-supported) group does not mean that they will collaborate well!

Pitfalls of collaborative learning

 What characterizes “good” computer-supported collaborative learning?

Overview

Cognitive, social, and affective aspects of collaboration quality in CSCL

Communication (Clark & Brennan, 1991)

Grounding

adapting utterances to the amount of shared knowledge/ perspective/ experience

establishing referential identity (e.g. of objects in a shared whiteboard, of previous messages/ contributions)

establishing a shared terminology

Conversation management

initiating conversations

managing turn-taking

ensuring that contributions are taken up

For additional literature/ references, please see Meier, Spada, & Rummel, 2007

Joint information-processing

Elaborative information-processing

eliciting and providing elaborated explanations

using the partner as a resource

elaborating on partners’ contributions

Argumentative information-processing

constructing justified arguments and counterarguments

engaging in a critical discussion: avoiding an illusion of consensus

Coordination (explicit or tacit) (e.g. Malone & Crowstone, 1994)

Task division

identifying interdependent subtasks

blending individual and collaborative work

Time management

agreeing on a realistic time schedule

monitoring the remaining time during the work process

Resource management

handling the available tools efficiently

agreeing on who may use a technical feature at what time

Relationship management

maintaining equal participation

symmetric or complementary, depending on role structure

solving conflicts constructively

epistemic vs. social conflicts

Motivation

individual task orientation

keeping up a high level of expended effort

volitional processes: focusing attention, exerting motivation control

mutual self-regulation

mutual encouragement

monitoring performance and giving feedback

Example: Collaboration quality rating-scheme

Development

sample from study on interdisciplinary collaboration : students of psychology and medicine solving complex patient cases (Rummel & Spada, 2005)

Meier, A., Spada, H. & Rummel, N. (2007). A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning, 2 , 63-86.

Example: Collaboration quality rating-scheme Control Room Experimental Room I Experimental Room II

Development

sample from study on interdisciplinary collaboration : students of psychology and medicine solving complex patient case (Rummel & Spada, 2005)

data- and theory-driven analyses  5 aspects/ 9 dimensions

for each dimension:

collaboration “standard” defined and illustrated in rating handbook

collaboration quality rated on 5-point scales

Example: Collaboration quality rating-scheme model / script > control model-plus > model Information pooling Task division Time management Technical coordination model > control > script model-plus > model Individual task orientation Quality of joint solution (Rummel, Spada, & Hauser, 2009) Rummel rummele

adaptation to new CSCL setting (Synergo) (Voyiatzaki et al., 2008)

descriptive framework valid in this setting as well

But: changed operationalization of dimensions and re-anchoring of scales necessary

… .. work in progress:

providing adaptive feedback to students based on ratings of their collaboration quality

(Meier, Voyiatzaki, Kahrimanis, Rummel, Spada, Avouris, 2008)

 How can beneficial collaboration processes be facilitated?

Overview

Earlier approaches: support “around” collaboration

Collaboration scripts: support during collaboration

Adaptivity: from fixed to flexible support

Support prior to collaboration, e.g. training for strategic questioning (King, 1991)

Support after collaboration, e.g. group processing approaches (Yager, Johnson, Johnson, & Snider, 1986)

after: Diziol & Rummel, accepted

provide specific instructions about task-related interaction (Kollar et al., 2006)

Sequencing work phases

Distributing roles

Specifying activities

 goal: enhance cognitive, meta-cognitive and social learning processes

Collaboration Scripts

S plit W here I nteraction S hould H appen (SWISH) ( Dillenbourg & Jermann, 2007)

Schema Split Compensation mutual regulation cognitive & metacognitive tasks (e.g. recaller & detector) Reciprocal argumentation conflicting opinions (e.g. pro & contra-roles) Conflict exchange of information distribution of knowledge (e.g. expert groups & teams) Jigsaw

Adaptivity: from fixed scripts to flexible support

Danger of “overscripting” collaboration (Dillenbourg, 2002); instead: taking into account students’ prior knowledge and “internal collaboration scripts”

realizing flexible, adaptive support:

“ Wizard of Oz” studies

adaptive feedback based on automated analyses of interaction (e.g. Dönmez et al, 2005)

Overview

Example: Supporting Collaborative Inferences F - I - R - E ! Figure from: Bauer, K., & Hesse, F. (2006). Von Kopf zu Kopf. [From head to head]. Gerhirn und Geist [Brain & Mind], 5/2006, 34-39.

Example: Supporting Collaborative Inferences Wolfgang‘s fingerprints are on the gun. Wolfgang showed the guns to his guests in the afternoon. A B

Example: Supporting Collaborative Inferences Wolfgang left his fingerprints on the weapon when he showed it to his guests. A B

Example: Supporting Collaborative Inferences Meier & Spada, 2007 Person B Person A shared individual collaborative Inference type Information distribution ** *

Example: Supporting Collaborative Inferences

Why is it so difficult to draw collaborative inferences?

individual group member holds “unconnected” information

seen as less relevant and therefore less likely to be brought up during discussion (Fraidin, 2004)

inference must be drawn on the basis of newly learned information and recalled information

people tend to focus on old rather than new information (Wittenbaum, Hubbel & Zuckermann, 1999)

recall vulnerable to disruptions in group discussion (Finlay, Hitch & Meudell, 2000)

Training Experiment: train collaboration strategies for

drawing inferences

pooling “unconnected” information

taking up new information

Example: Supporting Collaborative Inferences Meier & Spada, 2008 Test task (murder mystery) No Training (n=9 ) Testing phase collaborative reflection ... with inference tutoring tool discussion & solution Training task (medical diagnosis): individual reading phase read text on collaboration strategies Training phase Training Task + Text + Tutoring (n=9) Training Task+ Text (n=9) Training Task (n=9)

Example: Supporting Collaborative Inferences 6 7 5 8 4 2 ... 3 1 inference patient information disease information New Information! ANJA has matching information.

Example: Supporting Collaborative Inferences 6 7 5 8 4 2 ... 3 1 inference patient information disease information Well done! You have drawn an important inference!

Example: Supporting Collaborative Inferences Meier & Spada, 2008 Test task (murder mystery) No Training (n=9 ) Testing phase collaborative reflection ... with inference tutoring tool discussion & solution Training task (medical diagnosis): individual reading phase read text on collaboration strategies Training phase Training Task + Text + Tutoring (n=9) Training Task+ Text (n=9) Training Task (n=9)

Example: Supporting Collaborative Inferences ** ** performance during testing (without tutoring tool) Meier & Spada, 2008

In a nutshell…

diverse perspectives on collaborative learning within field of CSCL

successful collaboration does not occur spontaneously!

focus here was on processes, rather than outcomes or preconditions

many relevant aspects: communication, information-processing, coordination, relationship management, motivation

collaboration scripts: (computer-)support during collaboration

moving towards more flexible, more adaptive support

collaborative inferences are important but difficult

adaptive support yields best training results

Many thanks to the CoEmCo-Team: Hans Spada, Nikol Rummel Dejana Diziol, Sabine Hauser Eva Zerpies, Malte Jansen This work was funded by Thank you for your attention!

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Diziol, D., & Rummel, N. (to appear). How to design support for collaborative e-learning: A framework of relevant dimensions. In: Ertl, B. (Ed.): E-Collaborative knowledge construction: learning from computer-supported and virtual environments.

Dönmez, P., Rose, C., Stegmann, K., Weinberger, A., & Fischer, F. (2005). Supporting CSCL with automated corpus analysis technology. In T. Koschmann, D. Suthers, & T. W. Chan (Eds.), Proceedings of the CSCL 2005 (pp. 125–134). Mahwah, NJ: Lawrence Erlbaum Associates.Kollar, I., Fischer, F., & Hesse, F. W. (2006). Collaboration scripts - a conceptual analysis. Educational Psychology Review. Springer.

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Meier, A., Voyatzaki, E., Kahrimanis, G., Rummel, N., Spada, H., & Avouris, N. (2008). Teaching students how to improve their collaboration: Assessing collaboration quality and providing adaptive feedback in a CSCL setting. Symposium New Challenges in CSCL: Towards adaptive script support (N. Rummel & A. Weinberger), ICLS 2008, Utrecht, The Netherlands.

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References / Readings

Example: Collaboration quality rating-scheme Meier, A., Spada, H. & Rummel, N. (2007). A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning, 2 , 63-86.

Time management

Information pooling

Joint information processing

Reaching consensus

Sustaining mutual understanding

Communication

Task division

Coordination

Technical coordination

Individual task orientation

Motivation

Reciprocal interaction

Relationship management

Dialog management

Outcomes of collaborative learning

When are groups better than individuals? insights from social psychology (Kraut, 2003)

Aggregation: combining the unique resources of individual group members

Making use of members’ complementary knowledge, perspectives, skills etc.

e.g. a cross-functional marketing team making strategic decisions based on members’ complementary expertise

Synergy: going beyond the resources contributed by group members

building on each others’ contributions, creating innovative ideas & solutions

e.g. a product-design team developing a new product

“ assembly bonus”

however: groups tend to neglect members’ unique knowledge and focus instead on shared knowledge (Stasser & Titus, 1985)

Measuring the success of computer-supported collaborative learning

Individual learning

types of knowledge and skills

conceptual vs. procedural

skills: domain, collaboration, self-regulation, computer-literacy

level of evaluation: subjective evaluation - retention – transfer

Group-level learning

transactive memory, shared mental models

 facilitates future collaboration in the same group

Interpersonal and motivational outcomes

trust, liking, friendships,…

self- and group-efficacy

interest

 motivation for future collaboration

Measuring the success of computer-supported collaborative learning

What characterizes “success” in the TEL-environment you study? How do you assess it?

Assessing collaboration quality cognitive, social, and affective processes process gain: assembly bonus, synergy effects process loss: motivation and coordination problems group size & composition task type technical & informational resources goal structure Group learning, e.g. transactive memory, shared mental models Individual learning - domain knowledge and problem-solving skills - collaboration skills - technical skills Interpersonal & motivational outcomes, e.g. trust, liking, self-efficacy, group-efficacy institutional context Input