Regulative support for collaborative scientific inquiry learning Presenter: Hou, Ming-Hsien Professor: Ming-Puu Chen Date: August 19, 2008 Manlove, S., Lazonder, A.W. & Jong, T. (2006). Regulative support for collaborative scientific inquiry learning. Journal of Computer Assisted Learning. 22(2),

Introduction A review by De Jong and Van Joolingen (1998) showed that the effectiveness of inquiry learning is challenged by intrinsic problems many students have with this mode of learning. Collaborative inquiry learning requires high degrees of cognitive regulation, in that students have to plan a series of experiments, monitor progress and comprehension, and evaluate their inquiry learning processes and knowledge gains. The current research attempts to offer empirical evidence regarding the potentials of online tool support for regulation during collaborative inquiry learning.

Self-regulation framework(1/2) Most cognitive regulation models distinguish three phases within the cyclical process of self-regulation, namely planning, monitoring, and evaluating. In the planning phase students engage in problem orientation, goal setting, and strategic planning. Throughout the execution of a strategic plan, students monitor what they are doing to ensure that they are making progress towards the specified goals. During the evaluation phase, students assess both the processes employed and the products achieved

Self-regulation framework(2/2) Students should be promoted and directed to (1)Set goals that reflect the phases of scientific inquiry (2)Form a strategic plan by setting subgoals (3)Highlight strategies to achieve these subgoals (4)Monitor progress by taking notes and appending these to goals and subgoals, and (5)Evaluate both their inquiry learning processes and their models utilizing a report template and standards implicit in goal hierarchies. The study reported below sought to determine if online tool support designed according to these implications promotes students’ regulatory activities and learning.

Method Participants Sixty-one high-school students (aged 16–18) worked in 19 triads and two dyads formed by track ability matching. 10 PC+ groups and 11 PC- groups. In the experimental condition (PC+), regulative directions were embedded within the tool. In the control condition (PC–) were given a similar version of this tool; however, it contained no regulative directions. PC ： Process Coordinator

Method Materials Fluid dynamics that invited them to discover which factors influence the time to empty a water tank. Regulation of the inquiry learning process was supported by the pc tool. In the pc– condition there were no preset (sub)goals, descriptions, hints, or report templates. Students in the pc+ condition received a version of this tool that contained a set of goals and subgoals that outlined the phases students should go through in performing their inquiry

Fig 1. Goal tree view (left), history view (middle), and report view (right) of the Process Coordinator (PC) used by the PC+ groups. Method Materials

Method 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. The modelling tutorial familiarized students with systems dynamic modelling language and the operation of Co-Lab’s modelling tool. In the next two lessons, students worked on the inquiry task.

Method Coding and scoring Models convey students’ conceptual domain knowledge from variable and relationship specification, and demonstrate scientific reasoning through overall model structure Learning outcomes were assessed from the number of correctly specified variables and relations in the models created by the groups of students. Students’ use of the PC tool was scored from the log files. Verbal interaction was scored from the chat history files using an iterative approach.

Fig 2. Reference model for the experimental task.

Method Data analysis Data analysis focused on between-group differences in learning outcomes and learning activities. Given the relatively small sample size and the skewness of some distributions, between-group differences were analysed by nonparametric Mann– Whitney U-tests. Correlations were computed between learning activities and learning outcomes. Subsequent qualitative analyses were conducted to shed light on the nature of the students’ discussions and resolve issues that remained unclear from the quantitative analyses.

Results Learning outcomes & Learning activities

Results Correlations

Results Qualitative analyses of verbal interaction The support offered by the PC+ reduced the need for regulative talk. PC+ groups relied on their own discussions to monitor their progress and task comprehension.

Discussion(1/4) The regulative directions in the PC+ reducing the need for lengthy discussions to develop task understanding and strategic plans. Encourage student engagement with model structure in intermediate phases of their inquiry learning.

Discussion (2/4) The regulative guidelines within the PC+ did not lead to higher instances of monitoring and evaluating. Probably due to a lack of time to complete the task. Embedding regulative support for monitoring via note taking in a manner more consistent with task execution may be a more fruitful option.

Discussion (3/4) Suggestion ： examine whether system-generated prompts can promote PC use during intermediate and final stages of an inquiry. Investigate where feedback loops could be augmented as more ‘natural’ prompts in the other tools within the environment in order to enhance the use of the PC.

Discussion (4/4) Two potential problems for designing regulative support tools. Support might take the place of regulative activities rather than scaffold them. The problem of metacognitive awareness: students often are ignorant of their needs for assistance or approach a task inefficiently.