Inquiry Science education and training are necessary to motivate students to science

Inquiry Science education and training are necessary to motivate students to science

Inquiry Science education and training are necessary to motivate students to science


Currently there is a general consensus that inquiry-based approaches to learning science that incorporate students’ active investigation and experimentation are necessary to motivate students for science (Osborne & Dilon, 2008). 

Inquiry is the process in which students engage in the investigation of scientifically oriented questions, perform active experimentation, formulate explanations from evidence, evaluate their explanations in light of alternative explanations, and communicate and justify their proposed explanations (National 

Research Council, 2000). Beyond the motivational benefit, inquiry learning and its associated processes also have value of their own; inquiry learning generates knowledge and, if well supported, can be more effective than direct forms of instruction (Furtak, Seidel, Iverson, & Briggs, 2012). For these reasons, it is held that inquiry should be part of the science curriculum (e.g., 

National Research Council, 2000). Contemporary technology-based approaches to science learning provide students with ample opportunities for inquiry. Technology-based environments offer simulations, games, data sets, and/or remote and virtual laboratories for students' inquiry-related use. Inquiry calls for non-linear and interactive content that 

These technology-based environments are able to provide, so that their technological affordances are directly used for pedagogical purposes (de Jong, 2006). Evidence is accumulating that technology-enhanced learning environments for inquiry provide students with genuinely effective learning opportunities, and large-scale studies show that these inquiry environments outperform more 

Direct approaches to instruction on a variety of outcome measures (e.g.,Deslauriers & Wieman, 2011; Eysink et al., 2009; Marusić & Slisko, 2012). However, these promising results only materialize when the inquiry process is structured and scaffolded (Alfieri, Brooks, Aldrich, & Tenenbaum, 2011). Effective scaffolds come in many forms. 

1- Examples include tools

 for creating hypotheses, data analysis tools, and tools for saving and monitoring experiments (see e.g., de Jong, 2005; Quintana et al., 2004; Zhang, Chen, Sun, & Reid, 2004). A growing number of computer-based inquiry environments have emerged that provide students with inquiry facilities together with an integrated supportive structure and scaffolds. 

Examples of such learning environments are: Inquiry Island (White et al., 2002); GenScope (Hickey, Kindfield, Horwitz, & Christie, 2003); SimQuest-based environments (de Jong et al., 1998); Co-Lab (van Joolingen, de Jong, 


his study compares learning from designing instruction in the context of simulation-based inquiry learning with learning from expository teaching. The domain of instruction was the electricity domain of high-pass and low-pass filters. Participants were students from a technical vocational school. 

In the experimental condition (N = 21) students created assignments for an imaginary student to help this student to learn from a computer simulation. The LOOK-EXPERIMENT-DESIGN (LED) approach was developed to support students in designing these assignments. 

This support structure scaffolded students in orienting themselves in the simulation (LOOK), in performing experiments to gain more insight into the simulated domain (EXPERIMENT), and in designing assignments (DESIGN) about the simulated domain. 

Students in the control condition (N = 28) received traditional instruction. Students came from two different classes and were divided over the two conditions. After 3 two-hour lessons, all students completed a test measuring conceptual and procedural knowledge. 

Results showed that, in one class, students who learned by designing assignments performed significantly better on test items measuring conceptual knowledge than students who learned from traditional instruction. This was not replicated in the other class. No differences between the conditions were found for procedural knowledge.

3- Lazonder, Savelsbergh, & Manlove, 

2005); WISE (Linn, Davis, & Bell, 2004); STOCHASMOS (Kyza, Constantinou, & Spanoudis, 2011); and SCY (de Jong et al., 2012). One approach that has not as yet been explored is to encourage learning from simulations by having students create scaffolds for other learners. 

This method is based on the idea of “learning by teaching”, which assumes that in aiming to teach others tutors are encouraged to learn the domain very thoroughly themselves. From research on peer tutoring, we know that tutors gain knowledge from their teaching experience; due to their need to explain to and question the tutee, tutors engage in processes requiring reflection about or summarization of their own knowledge (Roscoe & Chi, 2007). 

4- This "learning by teaching"

 approach has been used in technology-enhanced learning in the “Betty’s brain” software (e.g., Leelawong & Biswas, 2008). Here students learn by instructing a teachable agent, a graphical computer character equipped with artificial intelligence. 

Studies with Betty’s brain show that teaching another is highly motivating and leads to better learning results than learning for yourself (Chase, Chin, Oppezzo, & Schwartz, 2009), and also that learning by teaching the agent leads to higher performance compared to traditional teaching methods (Biswas, Leelawong, Schwartz, Vye, & Teachable 

Agents Grp, 2005). Another example is SimStudent; in SimStudent students learn by instructing a simulated student (Matsuda et al., 2010). Recent work has shown that students who teach SimStudent can achieve considerable and efficient knowledge gains, specifically if they have to reflect about their own teaching actions (Matsuda et al., 2012). However, there is also research that indicates that learning by teaching can hinder learning. Atkinson, 

5- Derry, Renkl, and Wortham (2000)

, for example, summarize a number of studies in which creating explanations for another learner was compared to creating explanations for oneself, with the overall finding that the students who prepared for teaching someone else scored lower on knowledge tests. These authors attribute this result to higher levels of anxiety and lower intrinsic motivation. 

They also indicate that having experience with tutoring beforehand may yield greater benefits from creating explanations for others. In other recent work the importance of preparing students for their tutoring role is also emphasized (Matsuda et al., 2011). 

These lines of research are further explored in the current study, in which we compared learning by designing assignments for another (fictitious) student to complete in a computer simulation with learning from expository teaching. Within the present study an assignment is a question, the correct answer and an explanation of the answer. 

6- An example of a question in such assignment would be: 

What happens to the output voltage of the filter if you double the frequency? Normally, the question, the alternative(s), and the feedback for an assignment are designed by an instructional designer or teacher. In the current study the assignments were designed by students themselves, with the idea that they could learn from the design process. 

In a previous study (Vreman-de Olde & de Jong, 2004) students designed assignments related to a computer simulation on electrical circuits. Two-thirds of the designed assignments were about calculations and definitions. 

One-third of the designed assignments were about the discoveries students made with the simulation, but these assignments were rather superficial and mainly described simple effects. To support students in designing assignments, we developed a paper-and-pencil design sheet, that prompted student to generate an idea, transform the idea into an assignment, and evaluate the assignment (by running it in the software environment). 

7- Students using this design sheet designed more

 assignments about the relations in the simulated domain than non-scaffolded students. In addition, scaffolded students more precisely described relations and provided more explanations than the nonscaffolded students. However, no differences between the two groups were found on a knowledge test (Vreman-de Olde & de Jong, 2006). 

These results, the review by Atkinson et al. (2000), and recent work on peer tutoring (e.g., Tsivitanidou, Zacharia, & Hovardas, 2011) suggest that students need (more) detailed scaffolding for their assignment designing activities. 

In the present study, we compared an experimental condition, in which students designed assignments for a simulation on electrical circuits and were supported by a detailed design scaffold that guided the students through different steps (described more fully in the 

Method section), with a control condition. In the control condition students worked on the same learning content but followed traditional instruction in which the teacher used the blackboard for explanations and students completed calculation exercises. To assess students’ learning outcomes a knowledge test with different types of test items was administered. 

8- We expected that the experimental group 

would perform better than the control group on conceptual test items measuring insight into the cause-effect relations of the examined domain because they would gain insight into those relations by designing assignments. Second, we expected that students in the control condition would perform better than the experimental group on procedural (calculation) items because of their greater amount of practice in performing calculations. Although these prediction


seem straightforward, recent work shows that a focus on enhancing conceptual knowledge may also lead to an improvement in procedural knowledge (Kolloffel & de Jong, 2013). This result is explained by the phenomenon of bootstrapping (Schauble, 1996) or iterative knowledge development (Rittle-Johnson, Siegler, & Alibali, 2001), which refers to the idea that the acquisition of conceptual understanding and of procedural knowledge can in some cases mutually support and stimulate each other.

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