Regulative support for collaborative scientific inquiry learning

1. Presenter: Feng, Chia-Yen Advisor: Chen, Ming-Puu Date: August 5, 2008 Regulative support for collaborative scientificinquiry 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.

2. Abstract • This study examined whether online tool support for regulation promotes student learningduring collaborative inquiry in a computer simulation-based learning environment. • Sixty-onestudents worked in small groups to conduct a scientific inquiry with fluid dynamics. • Groupsin the experimental condition received a support tool with regulatory guidelines. • Controlgroups 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 andinitial planning. • Qualitative data elucidated how regulative guidelines enhanced learning andsuggests ways to further improve regulative processes within collaborative inquiry learningsettings.

3. 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 pertainsto the students’ability to regulate their own learning. • to plan aseries of experiments, monitor progress and comprehension,and evaluate their inquiry learning processesand knowledge gains.

4. Introduction (2/2) • These findings signal a need to assist students inregulating their scientific inquiries. • Offeredstudents a stepwise description of the inquiry learningprocess and paper worksheets to record the resultsobtained during each step(Njoo and De Jong ,1993). • Thinker Toolscurriculum to scaffold students’ inquiry and modellingactivities(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.

5. 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.

6. Self-regulation framework (2/2) • The study employed a randomized groupdesign with two conditions. Groups in both conditionsutilized a support tool called the Process Coordinator(PC) to regulate their inquiry. • The experimentalcondition (PC+): regulative directions were embeddedwithin the tool. • The control condition (PC–): were given a similar version of this tool; however, itcontained 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.

7. 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.

9. Method(2/4) • Procedure • The 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 inquirytask. They were seated in the computer lab withgroup members dispersed throughout the room in orderto prevent face-to-face communication. • Studentswere directed to begin by reading the assignment, touse the PC tool for planning and to use only the chatfor communication.

10. 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.

11. 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.

12. Results (1/3) Learning outcomes • Learning outcomes were indicated by the quality of the groups’ final model solutions.

13. 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.

14. Results (1/3) Correlations • Correlational analyses were performed to reveal how model quality scores relate to learning activities.

15. 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.

16. Discussion (2/2) • Problem-2 • metacognitive 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.