RUNNING HEAD : HEURISTICS TO FACILITATE DISCOVERY LEARNING Using Heuristics to Facilitate Discovery Learning in a Simulation Learning Environment in a Physics Domain

This article describes a study into the role of offering heuristic support in fa cilitating discovery learning in simulation-based learning. The study compares two simula tionbased learning environments that use heuristics to support the learners in discoverin g the physics domain of collisions. In one learning environment heuristics are only us ed to provide the learner with guidance derived from heuristics, without presenting the heuristics themselves, in the other the heuristics themselves are also made explicit to the learner. The results show that, with the help of the heuristics, learners in both conditions gain domain knowledge from pre-test to post test. Process analyses suggest that presenting the heuristics explicitly facilitates more self-re gulation in students. Relations between knowledge and process measures show a different pattern for the conditions: active learners in the explicit condition score relatively high on the pos ttest, and active learners in the implicit condition score relatively low on the posttest. HEURISTICS TO FACILITATE DISCOVERY LEARNING 3 Using Heuristics to Facilitate Discovery Learning in a Simulation Le arning Environment in a Physics Domain Discovery learning has seen resurgence in popularity over the last decade for a t l ast two reasons. One reason is a change in the field of education from a teacher-ce ntered education towards a more learner-centered approach. Discovery learning, wi th its emphasis on active learning and knowledge construction, fits rather well within t he latter approach. Another reason is that computers became known and available to a wider range of people, and as a result, people started to create powerful computer learning environments. One development was to build simulations of processes in the real world and use these environments for discovery learning. These environments have the advantage that they can provide both phenomenal and conceptual representations to the learners (Roth, Woszczyna, & Smith, 1996) Learners worki ng in a simulation-based discovery learning environment need to employ a number of activities in the learning environment. These activities include choosing va lues for variables in experiments, plotting values of variables to interpret results, a nswering questions, formulating hypotheses, and many more (Author, 1993; Friedler, Nachmias, & Linn 1990; Krajcik, et al., 2000; Kuhn, Black, Keselman, & Kaplan, 2000; Lewis, Bishay, & McArthur, 1993; White, 1993). If learners are able to carry out these activities, discovery learning will be motivating (Minstrell, 2000), a nd lead to integrated knowledge about the domain (Schauble, Glaser, Raghavan, & Reiner, 1991). However, it has been reported that learners are not able to discover knowledge about the domain because they experience problems with one or more of the activities. Problems have been identified related to the activities mentioned a bove HEURISTICS TO FACILITATE DISCOVERY LEARNING 4 (Author, 1998a; Klahr & Dunbar, 1988; Kuhn, Schauble, & Garcia-Mila, 1992; Quinn & Alessi, 1994; Reimann, 1991; Shute & Glaser, 1990), and learners should therefore be supported. In providing support for discovery learning to learners, one can try to each discovery skills, offer support during the discovery learning process from within the learning environment or offer support them in such a way that learners become competent at discovery learning. The first approach, teaching discovery, aims at teaching a general methodology before the engaging in the discovery activity. However, this does not necessarily lead to successful learning, as transfer from a general t eaching scenario to a specific learning context is not trivial. The second approach, built-in support aims at shielding learners from potential problems that they might encounter during discove ry learning. A common implementation of this approach is to augment the simulationbased learning environment with cognitive tools, that is, components in the learning environment that take over part of the process, for instance, by externalizing lea rning processes, or structuring the task (Lajoie, 1993; Author, 1999b). For example, a learning environment may contain dedicated notebooks that allow learners to take structured notes, tools to manipulate data, and/or forms with explicit steps for the learner (Author, 1997; Shute & Glaser, 1990). If applied and implemented carefully, the learner will be guided through the environment, and will be able to acquire knowledge about the domain along the way. A potential drawback of this approach is that the next time that the learner will be exposed to a similar situation, with less wellimplemented guidance, the previous experience is may not transfer to this new situation. The third approach can be best characterized as a combination of the firs t HEURISTICS TO FACILITATE DISCOVERY LEARNING 5 two, in which the function of guidance is to support the learner in the current situation, and to teach the underlying principles for transfer to new situations. A possible way for implementing this third approach is making use of heuristics. We define heuristics as rules of thumb that can help to reach a certain goal in a complex problem-solving situation, but that do not necessary lead to a solution. (Polya, 1945, 1971; Schoenfeld, 1979, 1985) Heuristics have characteristics that make them suitable for both guidance, and transfer purposes. In the latter case, the le arner must acquire explicit knowledge of the heuristics, meaning that the learning environment must present heuristics in an explicit way. For discovery learning, heuristics can provide a powerful means for making decisions in the learning process. As a typical discovery process does not allow for a complete and exhaustive analysis of the problem and the context, decisions need to be made based on incomplete information. Therefore, heuristics play an important role i shaping a discovery process Table 1 presents an overview of heuristics that were used in this study. They stem from an inventory by Sanders, Bouwmeester, and van Blanken (2000), who reviewed literature about problem solving, discovery learning, simulation-based learning, and machine discovery. ___________________ Insert Table 1 about here ___________________ A potential risk of using heuristics is that they can be fallible, i.e., they might lead to wrong decisions. This can be illustrated with VOTAT (vary one thing at a time) a heuristic related to experimental design (Tsirgi, 1980). This heurist ic is generally considered to be important and has been the focus of studies (Chen & Klahr , 1999; Schauble, Glaser, Duschl, Schulze, & John, 1995) or given as hint to students HEURISTICS TO FACILITATE DISCOVERY LEARNING 6 before (Rivers & Vockel, 1987) or during (Lavoie & Good, 1988) experimentation. The idea behind it is that by changing only one thing at a time differences bet ween the outcomes of the experiments can be attributed to the one thing that was changed. However, as Zohar (1995) argues, it does not work in situations with interacting variables. In such a situation it is necessary to look at situations in which more tha n one variable is changed. This means that, when introducing heuristics in a learni ng environment, one must stress their limitations as well. Heuristics can vary in scope; i.e., the range of situations in which they can be applied. Two dimensions influencing the scope of the heuristic are domain dependency and generality. A heuristic like ‘design experiments giving characteristic results’ is less domain dependent than ‘choose special cases, set any parameter to 1,2,3’ which only applies in domains with numerical variables. Domain independent heuristics have a broader scope than domain dependent heuristics. More general heuristics have a wider range of situations in which they can be applied than speci fic heuristics. A heuristic like ‘simplify the problem’ is general, whereas ‘construct an analog problem with less variables is more specific and, therefore, more restr icted in use. Varying the use of heuristics in a learning environment along both dimensions can make learners aware of the heuristics’ potential use. This can make both le arning and transfer more likely to occur, as both the heuristic itself as its domain of application can be observed by the learner. Even more important is to make heurist ics explicit to the learner. In a learning environment it is possible to embed a heuristic implicit, by providing the learner with hints and guidance that are derived from a heuristic, without presenting the heuristic itself. This may result in succes sful behavior within the learning environment, but in order to expect transfer to other HEURISTICS TO FACILITATE DISCOVERY LEARNING 7 domains or situations, also an explicit explanation of the rationale behind the heurist ic should be provided to the learner. Aims of the current study The general aim of the current study was to examine whether offering heurist ics for discovery learning can engage learners in a discovery learning process in w hich they learn about a domain and, through the heuristics, about the process of discovery learning. As the discovery learning process is complex, and ill defined, it provides a meaningful context for the application of heuristics. Without proper support, learning about the domain will not happen, and the discovery learning process will be frustrating instead of stimulating. The study explores differential effe cts of implicit and explicit presentation of heuristics in combination on knowledge and on discovery behavior.