One problem in ill-defined domains is accurately identifying the source of errors. Obtaining sufficient information about the error can be difficult because doing so may interfere with the learning task. In this paper we present the results of an experiment in the domain of German adjectives. We trialed a modified student interface that gathers more data during problem solving by requiring the ...

Structural features of learning tasks are relevant for problem solving but not salient for novice learners. Feedback in the form of Knowledge of Correct Response (KCR) during practice is expected to help learners recognize the structural features and to profit from learner control over the selection of learning tasks. A 2 x 2 factorial experiment (N = 118) was conducted to study the effects of ...

This paper looks at the impact of “Talking Aloud Pair Problem Solving” (TAPPS) on student performance in a productivity application course. The argument is that success working with productivity applications involves success solving problems. By enhancing students’ problem solving skills, TAPPS enhances student performance. The empirical evidence presented indicates that those students who foll...

Many AI domains are nonmonotonic. The resulting uncertainty is often dealt with by associating degree-of-belief ratings (conditional probabilities) with propositions. However, in some domains, a key issue is determining the subset of the available data that the agent should process to use as the basis for making a decision about how to act. Since belief ratings provide no sense of their \re-lia...

We analyze the antecedents of affective states in a simulation problem-solving environment, The Incredible Machine: Even More Contraptions, through quantitative field observations of high school students in the Philippines using that system. We investigate the transitions between affective states over time, finding that several affective states, including flow, boredom, and frustration, but not...

We investigate the relationship between a student’s affect and how he or she chooses to use a simulation problem-solving environment, using quantitative field observations. Within the environment studied, many students were observed gaming the system (cf. Baker et al, 2004), while few students engaged in off-task behavior. We analyze which affective states co-occur with gaming the system, and w...

Researchers of constructivist learning suggest that students should rather learn to solve real-world problems than artificial problems. This paper proposes a smart constructivist learning environment which provides real-world problems collected from crowd-sourcing problem-solution exchange platforms. In addition, this learning environment helps students solve real-world problems by retrieving r...

Studying examples is a natural and common way of learning, and students use examples extensively when acquiring new skills [1, 2]. However, the learning benefits of examples are unclear, some studies showing great benefits [3], but others indicating only ability to solve problems similar to the original examples [4, 5]. However, self-explaining examples, that is analyzing and generating justifi...