Browsing by Subject "Transfer of learning"
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Item Break down the walls : how the “folder effect” influences the transfer of learning(2011-05) He, Jingjie; Svinicki, Marilla D., 1946-; Markman, ArthurCategorizing knowledge into different disciplines and units may block knowledge within separate “folders”, which could limit its later retrieval and transfer to new contexts. To test this hypothesis, two experiments had been conducted. In one experiment, participants memorized a list of words with or without cuing which category these words belonged to. One week later, they were asked to recall all the positive adjectives, which required them to retrieve words that came from different categories. In the other experiment, participants read exactly the same story but embedded in two different subject domains or no context. A survey report was presented to test whether people from different contexts would have different transfer effect. The current study replicated previous results that successful transfer was hard to observe in the laboratory settings without explicit prompts. The memory test and transfer task in this study were too difficult and resulted into to the poor performance of the participants. The initial hypothesis had been neither supported nor rejected. To test the hypothesis, future studies could reduce the time interval between study and test, and modified the transfer task to lower the difficulty of the experiment.Item Learning analytics in large college courses : facilitating retention and transfer through targeted retrieval practice(2016-05) Raley, Nathaniel David; Beretvas, Susan Natasha; Butler, Andrew CoxSpaced retrieval practice is known to benefit both long-term retention and transfer of learning, two important goals of education. However, most classes are not designed in a way that facilitates frequent quizzing or revisiting previously covered topics; this is particularly true in higher education, where a small number of exams typically account for the bulk of a student’s grade. Recently, a large undergraduate course at the University of Texas has implemented a new class structure that replaces high-stakes tests with daily quizzes administered during class via computer; furthermore, quiz items previously answered incorrectly can appear at random on future quizzes. Together, these innovations are an excellent first step toward bringing spaced retrieval practice into the college classroom. However, I propose that technology can be further leveraged in classes such as these to more optimally choose repeated items. Given graded student quiz data from one semester of this course, I use Multidimensional Item Response Theory (MIRT) and Sparse Factor Analysis (SPARFA) to jointly estimate concepts underlying the items and each students’ mastery of these concepts. After comparing these factor-analytic methods, I also explore free-response and student chat data using basic natural language processing. It is concluded that techniques from learning analytics can help realize the full potential of spaced retrieval practice in the classroom by optimizing the selection of repeated items so as to target remediation. Furthermore, such techniques can be used to introduce variability into retrieval practice, encouraging a deeper understanding of the content which is more likely to transfer to novel problems.