For many forms of e-learning environments, the system's
behaviors can be viewed as a sequential decision process
wherein, at each discrete step, the system is responsible for
deciding the next system action when there are multiple ones
available. Each of these system decisions a ects the user's
successive actions and performance and some of them are
more important than others. Thus, this raises an open ques-
tion: how can we identify the critical system interactive de-
cisions that are linked to student learning from a long trajec-
tory of decisions? In this work, we proposed and evaluated
Critical-Reinforcement Learning (Critical-RL), an adversar-
ial deep reinforcement learning (ADRL) based framework to
identify critical decisions and induce compact yet e ective
policies. Speci cally, it induces a pair of adversarial policies
based upon Deep Q-Network (DQN) with opposite goals:
one is to improve student learning while the other is to hin-
der; critical decisions are identi ed by comparing the two
adversarial policies and using their corresponding Q-value
di erences; nally, a Critical policy is induced by giving op-
timal action on critical decisions but random yet reason-
able decisions on others. We evaluated the e ectiveness of
Critical policy against a random yet reasonable (Random)
policy. While no signi cant di erence was found between
the two condition, it is probably because of small sample
sizes. Much to our surprise, we found that students often
experience so-called Critical phase: a consecutive sequence
of critical decisions with the same action. Students were
further divided into High vs. Low based on the number of
Critical phases they experienced and our results showed that
while no signi cant was found between the two Low groups,
the High Critical group learned signi cantly more than the
High Random group.
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TIPP&SEE: A Previewing & Navigating Strategy for Use/Modify Scratch Activities
With many school districts nationwide integrating Computer Science (CS) and Computational Thinking (CT) instruction at the K-8 level, it is crucial that CS instruction be e ective for diverse learners. A popular pedagogical approach is Use!Modify!Create, which introduces a concept through a more
sca olded, guided instruction before culminating in a more open-ended project for student engagement. Yet, little research has gone into strategies that increase learning during the Use!Modify step. This paper introduces TIPP&SEE, a learning that further sca olds student learning during this step. Results from a quasi-experimental study show statistically-signi cant outperformance from students using the TIPP&SEE strategy on all assessment questions of medium and hard difficulty, suggesting its potential
as an e ective CS learning strategy
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- Award ID(s):
- 1760055
- NSF-PAR ID:
- 10158762
- Date Published:
- Journal Name:
- Presentation at the annual meeting of the American Educational Research Association
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The rapid expansion of K-12 CS education has made it critical to support CS teachers, many of whom are new to teaching CS, with the necessary resources and training to strengthen their understanding of CS concepts and how to effectively teach CS. CS teachers are often tasked with teaching different curricula using different programming languages in different grades or during different school years, and tend to receive different professional development (PD) for each curriculum they are required to teach. This often leads to a lack of deep understanding of the underlying CS concepts and how different curricula address the same concepts in different ways. Empowering teachers to develop a deep understanding of CS standards, and use formative assessments to recognize common student challenges associated with the standards, will enable teachers to provide more effective CS instruction, irrespective of the curriculum and/or programming language they are tasked with using. This position paper advocates supporting CS teacher professional learning by supplementing existing curriculum-specific teacher PD with standards-aligned PD that focuses on teachers' conceptual understanding of CS standards and ability to adapt instruction based on student understanding of concepts underlying the CS standards. We share concrete examples of how to design standards-aligned educative resources and instructionally supportive tools that promote teachers' understanding of CS standards and common student challenges and develop teachers' formative assessment literacy, all essential components of CS pedagogical content knowledge.more » « less
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Accepted Manuscript1.0
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