Indicators referring to Learning Strategies

II.1.1. Indicators referring to Learning Strategies

Learners may use a variety of different strategies when given a problem that requires scientific or strategic practices. Some are based on systematic premises of exploring the problem at hand, while some are based on motivational disposition of the learners.

Both strands have been followed in recent research activities to understand the effects of behavioral aspects of metacognition in inquiry learning.

Control of variables strategy (or VOTAT “Vary one thing at a time”)

This strategy is a typical behavior occurring when students systematically explore the dependencies between variables and the effect of modifictions of the variable values. Understanding the effects by isolating the changes, i.e. resetting all variables to a neutral value and changing one variable in a controlled manner or modifying one variable between succeeding experiments, shows a higher level strategy characteristic for scientific inquiry. This indicator is especially well suited for simulations and operational models, where different variables can be identified clearly and modified accordingly. Examples for computer environments that offer these kind of inquiry learning are CoLab (CoLab, 2005) and other applications allowing simulation of dynamic systems (Strobel, Hung, Jonassen, 2006). Recent research on defining and

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using this indicator has been presented in Wirth et al. (2005) and the follow up work in a Graduate Program for Research in Scientific Teaching.

Raw data

The indicator is based on the participant's actions in the learning support environment, and in more detail in the actions concerning the experimental space. Each action is logged with the values of all independent variables. Depending on the design of experimental space these values may be changed by textual input, sliders or choice elements (buttons, choice lists).

Data Processing

While processing the data, two situations characterising the VOTAT strategy may occur:

– all independent variables but one are set to a initial (“zero”) value, thus the one changed value represents the action of understanding this one influence factor; this situation can be identified indepently of other learner actions, thus it is easy to detect by querying or matching a pattern

– given a previous experiment/situation the considered situation exactly differs in the value of one independent variable (“ceteris paribus”); this has to take into account sequences of actions thus also considering relations between learner actions

In the work of Wirth et al. (2005) the identification of these situations have been realized by feeding the logs into a spreadsheet and querying with proprietary scripts. In recent work the logging has been modified to be in a structured XML-based format which provides a wide variety of other approaches of identifying these situations, such as in (Harrer et al., 2005) and also conversions into other formats using query engines or data mining techniques.

Visualization

At the moment the indicator has been mainly targeting the researcher and teacher of scientific inquiry processes. Thus visualization was a minor issue, using standard means of the diagrams provided within the proprietary application. With the above mentioned approaches more sophisticated visualizations, such as timelines with highlighted situations and their characteristic values, can be achieved, that might also be suitable for feeding the indicator back to the learner after the learning or even at runtime.

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Interpretation

The indicator mainly signals the situations characterising the VOTAT strategy, thus the interpretation if the learner uses higher-level metacognitive strategies is on the target user of the indicator, at the moment the researcher and teacher. The indicator has been validated in the large scale study PISA 2003 with 747 students of age 15.

Inquiry Method

Inquiry method has been successfully implemented in several learning environments (White, 1998; Wichmann, 2003) to continuously engage students in a cycle of scientific experimentation. Through systematically formulating a hypothesis, setting conditions and conducting a experiment and elaborating on the experimentation process, students are encouraged to use metacognitive strategies such as planning, evaluating and monitoring (Brown, 1984). The verbal representations that evolve through the experimentation process allow researchers to analyze students’ knowledge structures as well as metacognitive indicators such as monitoring statements (Chi, 1989) or evaluating statements (Wichmann, 2005).

Raw Data

These metacognitive statements are embedded in self-explanations, which have been formulated by the learner during the inquiry process.

Data processing

In the case of Wichmann et al. the explanations were logged and classified according to the phases of the inquiry process in which the explanation ocurred (e.g.

Hypothesis).

Learning Diary

Recently efforts to support metacognition as part of self-regulated learning have been focusing on implementing learning diaries in school and university settings.

Students are asked over a period of e.g. a semester to track their learning process. This can include questions such as “how much have you learned today”, “what are you planning to learn”.

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Raw Data

In Winter (2005) students use a web interface to produce the learning diary. These data is saved in a database and can be retrieved according to the categories that have been determined in the categorization schema.

Both for Inquiry Method and Learning Diary the understanding of the self-explanations is critical for interpreting the learning process. Thus language processing techniques might help to improve the support of metacognition by (pre-)processing the explanations and classifying them automatically. Typical techniques that can be used for that purpose will be discussed in the summary of this section.