With the nationwide emphasis on improving outcomes for STEM undergraduates, it is important that we not only focus on modifying classroom instruction, but also provide students with the tools to maximize their independent learning time. There has been considerable work in laboratory settings examining two beneficial practices for enhancing learning: spacing and self-testing. In the current study, we examine biology students' study practices, particularly in the context of these two behaviors. We specifically investigate whether a light-touch study skills intervention focused on encouraging spacing and self-testing practices impacted their utilization. Based on pre- and post-course surveys, we found that students report utilizing both beneficial and ineffective study practices and confirm that usage of spacing and self-testing correlates with a higher course grade. We also found that students in the section of the course which received the study skills intervention were more likely to report continued use or adoption of spacing and self-testing compared to students in control sections without the intervention. Surprisingly, we found that underrepresented minorities (URMs) under-utilize self-testing, and that our intervention helped to partially ameliorate this gap. Additionally, we found that URMs who reported self-testing earned similar course grades compared to non-URMs who also self-tested, but that there was a much larger drop in performance for URMs who did not self-test relative to non-URMs who also did not selftest. Overall, we would encourage instructors to dedicate class time towards discussing the merits of beneficial study practices, especially for students that have historically underperformed in STEM disciplines.

Representation and prediction of student navigational pathways, typically based on neural network (NN) methods, have seen their potential of improving instruction and learning under insufficient human knowledge about learner behavior. However, they are prominently studied in MOOCs and less probed within more institutionalized higher education scenarios. This work extends such research to the context of online college courses. Comparing student navigational sequences through course pages to documents in natural language processing, we apply a skip-gram model to learn vector embedding of course pages, and visualize the learnt vectors to understand the extent to which students' learning pathways align with pre-designed course structure. We find that students who get different letter grades in the end exhibit different levels of adherence to designed sequence. Next, we fit the embedded sequences into a long short-term memory architecture and test its ability to predict next page that a student visits given her prior sequence. The highest accuracy reaches 50.8% and largely outperforms the frequency-based baseline of 41.3%. These results show that neural network methods have the potential to help instructors understand students' learning behaviors and facilitate automated instructional support.

Student clickstream data can provide valuable insights about student activities in an online learning environment and how these activities inform their learning outcomes. However, given the noisy and complex nature of this data, an ongoing challenge involves devising statistical techniques that capture clear and meaningful aspects of students' click patterns. In this paper, we utilize statistical change detection techniques to investigate students' online behaviors. Using clickstream data from two large university courses, one face-to-face and one online, we illustrate how this methodology can be used to detect when students change their previewing and reviewing behavior, and how these changes can be related to other aspects of students' activity and performance.

This study assessed the impact of flipped instruction on students' out-of-class study time, exam performance, preference, motivation, and perceptions in two sections of a large undergraduate chemistry course. Flipped instruction caused a shift in student workload without appreciably changing the overall study time. The treatment impact on student performance gradually diminished over time, showing a small but statistically significant effect with the final exam. No marked interaction was identified, indicating that flipped instruction benefited students of diverse backgrounds uniformly. Students in the flipped section showed mixed feelings with about one fifth of them displaying polarized attitudes. Open-ended student survey responses revealed non-compliance with pre-class studying as a serious implementation issue: By slowing down the overall pace of the class, it negatively affected students with different study behaviors and characteristics in ways that partly explained the small, diminishing treatment effect and absence of marked interaction. (C) 2016 Elsevier Ltd. All rights reserved.

We investigated the effect of hint modality on students' eye movements on conceptual physics problems with diagrams. We recruited 57 students enrolled in a physics class for future elementary teachers. The participants were randomly assigned to conditions with no hints, visual hints, text hints, audio hints, and all possible hint modality combinations. We found that different hint modalities affect students' eye movements differently and the difference of students' eye movements relates to their problem-solving performance. The results of this study are different from the predictions based on Cognitive Theory of Multimedia Learning (CTML). Our results suggest that the cognitive process in physics problem solving may not be fully explained by CTML and therefore more research might be necessary in this area.