The recent groundswell of interest in computer science education across many countries has created a pressing need for computing teachers at the secondary level. To satisfy this demand, some educational systems are drawing from their pool of in-service teachers
trained in other disciplines. While these transitioning teachers can learn about computing pedagogy and subject matter at professional learning workshops, daily teaching experiences will also be a source of their learning. We studied a co-teaching program
where instructional responsibilities were distributed between teachers and volunteers from the tech industry to explore how specific teaching practices supported teacher learning, with a focus on pedagogical content knowledge (PCK). Through qualitative analysis
of questionnaire and interview data gathered from three teachers during one school year, we identified the practices they engaged in and how their learning related to the enactment of those practices. Our results highlight several factors that influenced the ways in which teaching practices provided participants with opportunities to learn PCK: (a) active participation of students and volunteers; (b) teacher’s level of content knowledge; (c) interdependent practices; and (d) immediacy of the classroom environment.
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Iterative teacher-aware learning.
In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the student, after getting familiar with the teacher’s instruction mechanism, can infer the teacher’s intention to learn faster. Recently, the benefits of integrating this cooperative pedagogy into machine concept learning in discrete spaces have been proved by multiple works. However, how cooperative pedagogy can facilitate machine parameter learning hasn’t been thoroughly studied. In this paper, we propose a gradient optimization based teacher-aware learner who can incorporate teacher’s cooperative intention into the likelihood function and learn provably faster compared with the naive learning algorithms used in previous machine teaching works. We give theoretical proof that the iterative teacher-aware learning (ITAL) process leads to local and global improvements. We then validate our algorithms with extensive experiments on various tasks including regression, classification, and inverse reinforcement learning using synthetic and real data. We also show the advantage of modeling teacher-awareness when agents are learning from human teachers.
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- Award ID(s):
- 2015577
- NSF-PAR ID:
- 10351380
- Date Published:
- Journal Name:
- Neural Information Processing Systems (NeurIPS 2021).
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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