Learning with Diagrams: Effects on Inferences and the Integration of Information

Abstract. Students studied materials about the human heart and circulatory system using either (a) text only, (b) text with simple diagrams, or (c) text with detailed diagrams. During learning, students self-explained [1] the materials. Explanations were transcribed, separated into propositions, and analyzed according to the type of learning process they represented. Results demonstrated that diagrams promoted inference generation but did not affect other learning processes (such as elaboration or comprehension monitoring). However, only simple diagrams promoted generation of inferences that integrated domain information. Results indicate that diagrams may be useful because they guide the learner to engage in the cognitive processes required for deep understanding

Latent Problem Solving Analysis as an explanation of expertise effects in a complex, dynamic task

Abstract Latent Problem Solving Analysis (LPSA) is a theory of knowledge representation in complex problem solving that argues that problem spaces can be represented as multidimensional spaces and expertise is the construction of those spaces from immense amounts of experience. The model was applied using a dataset from a longitudinal experiment on control of thermodynamic systems. When the system is trained with expert-level amounts of experience (3 years), it can predict the end of a trial using the first three quarters with an accuracy of .9. If the system is prepared to mimic a novice (6 months) the prediction accuracy falls to .2. If the system is trained with 3 years of practice in an environment with no constraints, performance is similar to the novice baseline

The real foundation of fictional worlds

I argue that judgements of what is ‘true in a fiction’ presuppose the Reality Assumption: the assumption that everything that is (really) true is fictionally the case, unless excluded by the work. By contrast with the more familiar Reality Principle, the Reality Assumption is not a rule for inferring implied content from what is explicit. Instead it provides an array of real-world truths that can be used in such inferences. I claim that the Reality Assumption is essential to our ability to understand stories, drawing on a range of empirical evidence that demonstrates our reliance on it in narrative comprehension. However, the Reality Assumption has several unintuitive consequences, not least that what is fictionally the case includes countless facts that neither authors nor readers could (or should) ever consider. I argue that such consequences provide no reason to reject the Reality Assumption. I conclude that we should take fictions, like non-fictions, to be about the real world

Fostering Program Comprehension in Novice Programmers - Learning Activities and Learning Trajectories

This working group asserts that Program Comprehension (ProgComp) plays a critical part in the process of writing programs. For example, this paper is written from a basic draft that was edited and revised until it clearly presented our idea. Similarly, a program is written incrementally, with each step tested, debugged and extended until the program achieves its goal. Novice programmers should develop program comprehension skills as they learn to code so that they are able both to read and reason about code created by others, and to reflect on their code when writing, debugging or extending it. To foster such competencies our group identified two main goals: (g1) to collect and define learning activities that explicitly address key components of program comprehension and (g2) to define tentative theoretical learning trajectories that will guide teachers as they select and sequence those learning activities in their CS0/CS1/CS2 or K-12 courses. The WG has completed the first goal and laid down a strong foundation towards the second goal as presented in this report. After a thorough literature review, a detailed description of the Block Model is provided, as this model has been used with a dual purpose, to classify and present an extensive list of ProgComp tasks, and to describe a possible learning trajectory for a complex task, covering different cells of the Block Model matrix. The latter is intended to help instructors to decompose complex tasks and identify which aspects of ProgComp are being fostered