Prior math achievement and inventive production predict learning from productive failure.

A frequent concern about constructivist instruction is that it works well, mainly for students with higher domain knowledge. We present findings from a set of two quasi-experimental pretest-intervention-posttest studies investigating the relationship between prior math achievement and learning in the context of a specific type of constructivist instruction, Productive Failure. Students from two Singapore public schools with significantly different prior math achievement profiles were asked to design solutions to complex problems prior to receiving instruction on the targeted concepts. Process results revealed that students who were significantly dissimilar in prior math achievement seemed to be strikingly similar in terms of their inventive production, that is, the variety of solutions they were able to design. Interestingly, it was inventive production that had a stronger association with learning from PF than pre-existing differences in math achievement. These findings, consistent across both topics, demonstrate the value of engaging students in opportunities for inventive production while learning math, regardless of prior math achievement.

The impact of artificial intelligence on learner-instructor interaction in online learning.

Artificial intelligence (AI) systems offer effective support for online learning and teaching, including personalizing learning for students, automating instructors' routine tasks, and powering adaptive assessments. However, while the opportunities for AI are promising, the impact of AI systems on the culture of, norms in, and expectations about interactions between students and instructors are still elusive. In online learning, learner-instructor interaction (inter alia, communication, support, and presence) has a profound impact on students' satisfaction and learning outcomes. Thus, identifying how students and instructors perceive the impact of AI systems on their interaction is important to identify any gaps, challenges, or barriers preventing AI systems from achieving their intended potential and risking the safety of these interactions. To address this need for forward-looking decisions, we used Speed Dating with storyboards to analyze the authentic voices of 12 students and 11 instructors on diverse use cases of possible AI systems in online learning. Findings show that participants envision adopting AI systems in online learning can enable personalized learner-instructor interaction at scale but at the risk of violating social boundaries. Although AI systems have been positively recognized for improving the quantity and quality of communication, for providing just-in-time, personalized support for large-scale settings, and for improving the feeling of connection, there were concerns about responsibility, agency, and surveillance issues. These findings have implications for the design of AI systems to ensure explainability, human-in-the-loop, and careful data collection and presentation. Overall, contributions of this study include the design of AI system storyboards which are technically feasible and positively support learner-instructor interaction, capturing students' and instructors' concerns of AI systems through Speed Dating, and suggesting practical implications for maximizing the positive impact of AI systems while minimizing the negative ones.

DIY productive failure: boosting performance in a large undergraduate biology course.

Students in first-year university courses often focus on mimicking application of taught procedures and fail to gain adequate conceptual understanding. One potential approach to support meaningful learning is Productive Failure (PF). In PF, the conventional instruction process is reversed so that learners attempt to solve challenging problems ahead of receiving explicit instruction. While students often fail to produce satisfactory solutions (hence "Failure"), these attempts help learners encode key features and learn better from subsequent instruction (hence "Productive"). Effectiveness of PF was shown mainly in the context of statistical and intuitive concepts, and lessons that are designed and taught by learning scientists. We describe a quasi-experiment that evaluates the impact of PF in a large-enrollment introductory university-level biology course when designed and implemented by the course instructors. One course-section (295 students) learned two topics using PF; another section (279 students) learned the same topics using an active learning approach, which is the standard in this course. Performance was assessed on the subsequent midterm exam, after all students had ample opportunities for practice and feedback, and after some time has elapsed. PF students scored nearly five percentage-points higher on the relevant topics in the subsequent midterm exam. The effect was especially strong for low-performing students. Improvement on the final exam was only visible for low-performing students. We describe the intervention and its potential to transform large introductory university courses.