Regulative support for collaborative scientific inquiry learning

Manlove, S., Lazonder, A.W., & de Jong ,T. (2006). Regulative support for collaborative scientific inquiry learning. Journal of Computer Assisted Learning, 22(2), 87-98.

Presenter: Feng, Chia-Yen

Advisor: Chen, Ming-Puu

Date: August 5, 2008

Abstract This study examined whether online tool support for regulation

promotes student learning during collaborative inquiry in a computer simulation-based learning environment.

Sixty-one students worked in small groups to conduct a scientific inquiry with fluid dynamics. Groups in the experimental condition received a support tool with

regulatory guidelines. Control groups were given a version of this tool from which these

instructions were removed. Results

To showed facilitative effects for the fully specified support tool on learning outcomes and initial planning.

Qualitative data elucidated how regulative guidelines enhanced learning and suggests ways to further improve regulative processes within collaborative inquiry learning settings. 2

Introduction (1/2)

Science learning as students working in groups to perform experiments and build computer models to induce, express, and refine scientific knowledge.

The effectiveness of inquiry learning is challenged by intrinsic problems many students have with this mode of learning (De

Jong and Van Joolingen , 1998). These problems are usually addressed by cognitive tools:

support structures which aim to compensate for students’ knowledge or skill deficiencies.

Another class of problems pertains to the students’ ability to regulate their own learning. to plan a series of experiments, monitor progress and comprehension,

and evaluate their inquiry learning processes and knowledge gains. 3

Introduction (2/2)

These findings signal a need to assist students in regulating their scientific inquiries. Offered students a stepwise description of the inquiry learning process

and paper worksheets to record the results obtained during each step (Njoo and De Jong ,1993).

Thinker Tools curriculum to scaffold students’ inquiry and modelling activities (White et al. 1999).

Utilized system-generated prompts to direct students’ attention to the regulatory aspects of their inquiry task (Veenman et al. , 1994).

Such online tool support typically combines regulative hints and explanations with electronic facilities for students to record, monitor, and evaluate their own plans, hypotheses, experimental data, and models.

The current research therefore attempts to offer empirical evidence regarding the potentials of online tool support for regulation during collaborative inquiry learning.

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Self-regulation framework (1/2)

Models of self-regulation define the metacognitive processes and strategies expert learners use to improve learning (e.g. Butler & Winne 1995; Schraw 1998; Zimmerman 2000).

Most cognitive regulation models distinguish three phases within the cyclical process of self-regulation, namely planning, monitoring, and evaluating. Planning: students engage in problem orientation, goal setting, and

strategic planning. Monitor: Throughout the execution of a strategic plan, students

monitor what they are doing to ensure that they are making progress towards the specified goals (Ertmer & Newby 1996).

Evaluation: evaluation of learning products involves student assessment of learning objects and outcomes they have created.

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Self-regulation framework (2/2)

The study employed a randomized group design with two conditions. Groups in both conditions utilized a support tool called the Process Coordinator (PC) to regulate their inquiry. The experimental condition (PC+): regulative directions were

embedded within the tool. The control condition (PC–) : were given a similar version of this

tool; however, it contained no regulative directions. In this study collaboration was chosen as a context for inquiry

learning. Collaboration in inquiry leads to improved inquiry processes and better results (cf. Okada & Simon 1997) and relates positively to self-regulation.

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Method (1/4)

Participants Sixty-one high-school students (aged 16–18) worked in 19 triads

and two dyads formed by track ability matching. Subsequent random allocation of student groups to conditions resulted in 10 PC+ groups and 11 PC– groups.

Materials Groups in both conditions worked on an inquiry task within fluid

dynamics that invited them to discover which factors influence the time to empty a water tank.

This task was performed within Co-Lab, a collaborative discovery learning environment in which the groups could experiment through a computer simulation of a water tank and express acquired under understanding in a group developed, runnable, system dynamics model.

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Method (2/4)

ProcedureThe experiment was conducted over three weekly 1 hour

lessons that were run in the school’s computer lab. The first lesson involved a guided tour of Co-Lab and an

introduction to modeling. In the next two lessons (hereafter: session 1 and session 2)

students worked on the inquiry task. They were seated in the computer lab with group members dispersed throughout the room in order to prevent face-to-face communication.

Students were directed to begin by reading the assignment, to use the PC tool for planning and to use only the chat for communication.

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Method (3/4)

Coding and scoring Learning outcomes were therefore assessed from the number of

correctly specified variables and relations in the models created by the groups of students.

Concerning relations, one point was awarded for each correct link between two variables. The maximum model quality score was 26.

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Method (4/4)

Students’ use of the PC tool was scored from the log files. PC actions associated with planning

(1)viewing of specific goals, (2) adding goals or subgoals, (3) viewing hints, and (4) viewing the goal descriptions.

Monitoring was defined by three actions (1)adding notes to goals, (2) marking goals complete,

and (3) checking the history. Evaluation was assessed from

(1) generating the report by clicking the corresponding tab and (2) writing within the report.

Verbal interaction was scored from the chat history files using an iterative approach.

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Results (1/3)

Learning outcomes Learning outcomes were indicated by the quality of the

groups’ final model solutions.

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Results (1/3)

Learning activities

Analyses of learning activities focused on the groups’ use of the PC tool and their verbal interactions.

planning PC+ group viewed goals sparingly while another group excessively

consulted goal descriptions. Monitoring

students in the PC+ condition used the PC for monitoring purposes just as often as their PC– counterparts did.

Verbal interaction data were analysed to examine whether groups in both conditions talked differently about the task and its regulation.

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Results (1/3)

Correlations

Correlational analyses were performed to reveal how model quality scores relate to learning activities.

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Discussion (1/2)

One suggestion would be to examine whether system-generated prompts can promote PC use during intermediate and final stages of an inquiry

Problem-1 that support might take the place of regulative activities

rather than scaffold them providing students with complete goal lists, for example, may cause them to simply follow these directions rather than think about how to approach the task.

Future research should address the fine line exemplified here between scaffolding and replacing regulative processes. 15

Discussion (2/2)

Problem-2metacognitive awareness: students often are ignorant of

their needs for assistance or approach a task inefficiently especially in light of the multiple, recursive activities involved in inquiry learning.

Future research needs to address whether or not imposed use of a regulative support tool at key points within and across sessions might raise students’ awareness of the difficulties they are having and how to correct them.

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