What are you talking about?: A Cognitive Task Analysis of how specificity incommunication facilitates shared perspective in a confusing collaboration task

This study investigated how participant’s specificity in shar-ing of information in collaborative problem solving was criti-cal to them reaching a successful shared perspective. We ana-lyzed participants’ communication strategies in a collaborativetask designed to make finding common ground challenging.We set out to better understand the difference between suc-cessful and unsuccessful collaborations by conducting a cog-nitive task analysis. From participants’ utterances, we inferredcognitive processes associated with repeating communicationmoves and coded those processes as if-then production rules.We thereby specified the communication strategies used duringinteractions and developed a production-rule model to explainwhether and how shared perspective developed or not. Ourcognitive task analysis indicated that although all collaboratingpairs described the objects they were seeing with a variety offeatures, the successful pairs were more specific in using com-binations of features. Quantitatively, we found significant cor-relations between frequency of combined feature statementsand success in sharing perspectives

Perceptual Chunks in Geometry Problem Solving: A Challenge to Theories of Skill Acquisition

In current theories of skill acquisition it is quite common to assume that the input to learning mechanisms is a problem representation based on direct translations of problem instructions or simple inductions from problem solving examples. W e call such a problem representation an execution space because it is made up of operators corresponding to the external actions agents perform while executing problem solutions. Learning proceeds by modifications and combinations of these execution space operators. W e have built a model of geometry expertise based on verbal report evidence which contains operators which can be described as modifications (e.g., abstractions) and combinations (e.g., compositions) of execution operators. However, a number of points of evidence lead us to conclude that these operators were not derived from execution space operators. In contrast, it appears these operators derive from discoveries about the structure and properties of domain objects, particularly, perceptual properties. We have yet to develop a detailed and integrated theory of this "perceptual churJdng", but we present the exp)ert model is a challenge to ciurent dieories of skill acquisition

Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming

The emergence of large-language models (LLMs) that excel at code generation and commercial products such as GitHub's Copilot has sparked interest in human-AI pair programming (referred to as "pAIr programming") where an AI system collaborates with a human programmer. While traditional pair programming between humans has been extensively studied, it remains uncertain whether its findings can be applied to human-AI pair programming. We compare human-human and human-AI pair programming, exploring their similarities and differences in interaction, measures, benefits, and challenges. We find that the effectiveness of both approaches is mixed in the literature (though the measures used for pAIr programming are not as comprehensive). We summarize moderating factors on the success of human-human pair programming, which provides opportunities for pAIr programming research. For example, mismatched expertise makes pair programming less productive, therefore well-designed AI programming assistants may adapt to differences in expertise levels.Comment: 8 pages (without references), 2 table

When to Block versus Interleave Practice?Evidence Against Teaching Fraction Addition before Fraction Multiplication

In practice, mathematics education is blocked (i.e., teachingone topic at a time; CCSS, 2010), but research generallypromotes interleaving (i.e., teaching multiple topics together;Rohrer & Taylor, 2007). For example, fraction arithmetic isblocked with students being taught fraction addition beforefraction multiplication. Since students often confuse fractionoperations to produce arithmetic errors, interleaved fractionarithmetic instruction might be more productive than blockedinstruction to teach students to discriminate between theoperations. Additionally, a cognitive task analysis suggeststhat fraction multiplication may be a prerequisite to fractionaddition and thus reversing the blocking order may enhancelearning. Two experiments with fraction addition and fractionmultiplication were run. Experiments 1 and 2 show thatinterleaved instruction is generally better than the currentblocked instruction. Experiment 2 provides evidence thatblocking that reverses the standard order -- providing practiceon fraction multiplication before fraction addition -- producesbetter learning

Simulated Students and Classroom Use of Model-Based Intelligent Tutoring

Two educational uses of models and simulations: 1) Students create models and use simulations ; and 2) Researchers create models of learners to guide development of reliably effective materials. Cognitive tutors simulate and support tutoring - data is crucial to create effective model. Pittsburgh Science of Learning Center: Resources for modeling, authoring, experimentation. Repository of data and theory. Examples of advanced modeling efforts: SimStudent learns rule-based model. Help-seeking model: Tutors metacognition. Scooter uses machine learning detectors of student engagement

A Cognitive Analysis of the Task Demands of Early Algebra

Mathematical problems presenting themselves in the workplace and in academia are often solved by informal strategies in addition to or instead of the normative formal strategies typically taught in school. By itself this observation does little to tell us whether, when and how much these techniques should be taught. To ground arguments about the appropriate role of altemative problem-solving techniques in education, we need to first understand the demands of the tasks they address. Our focus here is on algebra and pre?algebra, or, more specifically, on the set of problems that resist solution by more elementary arithmetic methods. W e present a task analysis of this set of problems that is based on the identification of mathematical and situational problem difficulty factors. These factors provide a framework for comparing the candidate representations and strategies to meet the demands of more complex problems. W e summarize the altemative techniques that have been observed in effective problem solving and discuss their relative strengths and weaknesses. The task analysis along with this comparative analysis provides a basis for hypothesizing developmental sequences and for informing instmctional design