Egbert G. Harskamp

Structured Collaboration versus Individual Learning in Solving Physics Problems.

The research issue in this study is how to structure collaborative learning so that it improves solving physics problems more than individual learning. Structured collaborative learning has been compared with individual learning environments with Schoenfeld’s problem‐solving episodes. Students took a pre‐test and a post‐test and had the opportunity to solve six physics problems. Ninety‐nine students from a secondary school in Shanghai participated in the study. Students who learnt to solve problems in collaboration and students who learnt to solve problems individually with hints improved their problem‐solving skills compared with those who learnt to solve the problems individually without hints. However, it was hard to discern an extra effect for students working collaboratively with hints—although we observed these students working in a more structured way than those in the other groups. We discuss ways to further investigate effective collaborative processes for solving physics problems.

Solving Physics Problems with the Help of Computer – assisted Instruction.

The main goal of most physics textbooks is to develop declarative and procedural knowledge. Exercises provide pupils with opportunities to apply this knowledge. However, when confronted with more complicated exercises many pupils experience difficulties in solving them. A computer program about the subject of forces was developed containing hints for the various different episodes of problem‐solving. A study was undertaken with a group taking part in the experiment (n = 11) who used both their textbook and the computer program, and a control group (n = 25) who used their textbook only. There was evidence to show that the pupils from the group taking part in the experiment did achieve higher results in solving problems. Exploration and planning were improved but evaluation was not. It appeared that pupils involved in the experiment made better use of their declarative knowledge in solving problems than pupils from the control group.

Improving mathematical problem solving : A computerized approach

Mathematics teachers often experience difficulties in teaching students to become skilled problem solvers. This paper evaluates the effectiveness of two interactive computer programs for high school mathematics problem solving. Both programs present students with problems accompanied by instruction on domain-specific knowledge required in different episodes of problem solving. The first program is based on a direct instructional approach to learning, the second on a constructivist view of learning. The latter approach is expected to be particularly beneficial to weak students. The effectiveness of both computer programs was evaluated by means of an experiment. Four classes worked with the constructivist based computer program, and four worked with the direct instructional program. Five classes that had received traditional mathematics education served as the control group. The computer programs were used in three periods of two consecutive weeks each. The results show that both computer programs improved problem-solving ability more strongly than had traditional mathematics instruction. Contrary to our expectations, both weak and skilled students benefited equally from both computer programs. Specifically, the programs helped the students to improve the quality of their analysis and verification skills during problem solving.

Collaboration and Peer Tutoring in Chemistry Laboratory Education

The aim of this study is to examine the effectiveness of collaborative learning with hints and peer tutoring with hints, and individual learning with hints in chemistry laboratory education in a secondary school. A total of 96 eleventh graders participated in this study. The study has a randomized pre‐test and post‐test design with a delayed post‐test. During the four‐week intervention, students were required to carry out eight lab tasks in total. The students filled in a 17‐item self‐assessment of learning gain questionnaire on the last day. Analyses of students’ learning achievements showed that students in both the collaborative learning and peer tutoring situations outperformed those learning individually with hints. The delayed post‐test, which was administered three months later, revealed that students who had been in the peer tutoring situation outscored those in the collaborative learning situation. Student self‐assessment questionnaires on learning gain provided further details in this regard.

The effect of the timing of instructional support in a computer-supported problem-solving program for students in secondary physics education

Many students experience difficulties in solving applied physics problems. Researchers claim that the development of strategic knowledge (analyze, explore, plan, implement, verify) is just as necessary for solving problems as the development of content knowledge. In order to improve these problem-solving skills, it might be profitable to know at what time during problem solving is the use of instructional support most effective: before, during or after problem solving. In an experiment with fifth-year secondary school students, one experimental group (n=18) received hints during and worked examples after problem solving, and another experimental group (n=18) received worked examples only after problem solving. Both groups used versions of a computer program to solve a variety of problems. The control group (n=23) used a textbook. There was a pre-test to estimate the measure of prior expertise of the students in solving physics problems. The results of a problem-solving post-test indicated that the version of the program providing hints during and examples after problem solving was the most effective, followed by the version which only supplied examples afterwards. There was no difference in effect for students with more than average prior knowledge or less prior knowledge.

Introduction to this special issue

The use of metacognitive instruction by teachers and its attendant impact on learning are important issues in today’s education. Educational policy-makers expect that metacognitive training will help students to become more self-regulative learners. The problem for teachers is how to coach students to make use of metacognitive skills. Students can be taught to use certain scaffolds during the learning process to help them to improve their analysis of the learning task, planning of activities, and regulation of their learning. But to be effective, these skills have to be implemented not only consistently but also on an individual basis. Thus, metacognitive feedback and support is often provided through computer programs. Such programs can aid teachers in having their students actually use metacognitive strategies during their learning processes (Harskamp & Suhre, 2007). Additionally, colleges of education which emphasize computer-based metacognitive training may help novice teachers to become aware of the value of metacognitive training, something they might convey to their students afterwards. This special issue has the aim to show what research by prominent researchers in the field of metacognitive training has to say about computer-supported metacognitive training and which questions deserve further attention. The main theme of the issue is ‘‘how to support student metacognition during learning with computer-based learning environments’’. Traditional computer-based learning environments such as intelligent tutoring systems monitor, adapt, and scaffold a learner’s individual learning. These systems tell students how to make their way through the process of learning. Recently, more open-ended learning environments have been introduced in which students are invited not only to make their way through and solve a problem or find a correct answer but also to learn how to seek help and how to apply this help in solving a problem. The emphasis in these environments is not only on getting the right answer but also on using and practicing metacognitive skills. In addition, such learning environments can encourage students to learn to do these metacognitive tasks cooperatively.