Browsing by Subject "Cognitive science"

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    From Teaching Democratic Thinking to Developing Democratic Civic Identity
    (Partnerships: A Journal of Service-Learning and Civic Engagement, 2015) Bringle, Robert G.; Clayton, Patti H.; Bringle, Kathryn E.
    Using theory and research from the cognitive and social sciences as well as the literature of service-learning and community-campus engagement, we critically examine an over-emphasis on democratic thinking as the primary construct of interest in American higher education’s efforts to prepare young people for meaningful participation in democracy. We propose developing democratic civic identity as a more appropriate superordinate goal than teaching democratic thinking. We examine relationships between and among cognition, behavior, and attitudes generally and within the context of democratically-engaged community-campus partnerships and democratic critical reflection as a basis for developing and refining persons as civic agents in a diverse democracy. We conclude with implications of the analysis for service-learning—a pedagogy that, when designed and implemented accordingly, provides a uniquely powerful means to cultivate democratic civic identity.
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    Post-Processing Automatic Transcriptions with Machine Learning for Verbal Fluency Scoring
    (Elsevier, 2023) Bushnell, Justin; Unverzagt, Frederick; Wadley, Virginia G.; Kennedy, Richard; Del Gaizo, John; Clark, David Glenn; Neurology, School of Medicine
    Objective: To compare verbal fluency scores derived from manual transcriptions to those obtained using automatic speech recognition enhanced with machine learning classifiers. Methods: Using Amazon Web Services, we automatically transcribed verbal fluency recordings from 1400 individuals who performed both animal and letter F verbal fluency tasks. We manually adjusted timings and contents of the automatic transcriptions to obtain "gold standard" transcriptions. To make automatic scoring possible, we trained machine learning classifiers to discern between valid and invalid utterances. We then calculated and compared verbal fluency scores from the manual and automatic transcriptions. Results: For both animal and letter fluency tasks, we achieved good separation of valid versus invalid utterances. Verbal fluency scores calculated based on automatic transcriptions showed high correlation with those calculated after manual correction. Conclusion: Many techniques for scoring verbal fluency word lists require accurate transcriptions with word timings. We show that machine learning methods can be applied to improve off-the-shelf ASR for this purpose. These automatically derived scores may be satisfactory for some applications. Low correlations among some of the scores indicate the need for improvement in automatic speech recognition before a fully automatic approach can be reliably implemented.