Five experiments are described on the processing of ambiguous words in sentences. Two classes of ambiguous words (noun-noun and noun-verb) and two types of context (priming and non-priming) were investigated using a variable stimulus onset asynchrony (SOA) priming paradigm. Noun-noun ambiguities have two semantically unrelated readings that are nouns (e.g., pen, organ); nounverb ambiguities have both noun and verb readings that are unrelated (e.g., tire, watch). Priming contexts contain a word highly semantically or associatively related to one meaning of the ambiguous word; non-priming contexts favor one meaning of the word through other types of information (e.g., syntactic or pragmatic). In non-priming contexts, subjects consistently access multiple meanings of words, and select one reading within 200 msec. Lexical priming differentially affects the processing of subsequent nounnoun and noun-verb ambiguities, yielding selective access of meaning only in the former case. The results suggest that meaning access is an automatic process which is unaffected by knowledge-based ("top-down") processing. Whether selective or multiple access of meaning is observed largely depends on the sfructure of the ambiguous word, not the nature of the context.

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Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students’ programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming.