Samenvatting

Problem-solving tasks form an important part of (higher education) curricula, especially in STEM-domains. For learners with little or no prior knowledge (novices), an effective way to learn new problem-solving tasks is by studying examples. These can be written out step-by-step solution procedures of a problem or teachers’ demonstrations of how to solve a problem. Nowadays, video examples are increasingly common. Moreover, students increasingly acquire problem-solving skills via computer-based learning environments in which examples and practice problems are presented. However, it is an open question how examples and practice problems can be best sequenced to foster novices’ motivation and learning outcomes. Moreover, relatively little is known about how (well) novices can self-regulate their learning with examples and practice problems. Both questions were addressed in this dissertation. Results showed that studying examples or alternating examples and practice problems, resulted in higher learning outcomes attained with less effort investment and more confidence in one's abilities than solving practice problems only. Moreover, starting with an example prior to practice problem solving resulted in more confidence in one's abilities and less effort investment than the other way around. When novices could select examples and practice problems themselves, they made choices that corresponded quite well with principles for effective sequencing known from instructional design research. Perhaps for that reason, instructing students on effective instructional design principles did not increase self-regulated learning outcomes. However, caution is needed when implementing self-regulated learning: even after instruction on effective principles, there still was room for improvement in students' task selections.