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Despite increased efforts to assess the adoption rates of open science and robustness of reproducibility in sub-disciplines of education technology, there is a lack of understanding of why some research is not reproducible. Prior work has taken the first step toward assessing reproducibility of research, but has assumed certain constraints which hinder its discovery. Thus, the purpose of this study was to replicate previous work on papers within the proceedings of the International Conference on Educational Data Mining to accurately report on which papers are reproducible and why. Specifically, we examined 208 papers, attempted to reproduce them, documented reasons for reproducibility failures, and asked authors to provide additional information needed to reproduce their study. Our results showed that out of 12 papers that were potentially reproducible, only one successfully reproduced all analyses, and another two reproduced most of the analyses. The most common failure for reproducibility was failure to mention libraries needed, followed by non-seeded randomness. All openly accessible work can be found in an Open Science Foundation project1. 

There have been numerous efforts documenting the effects of open science in existing papers; however, these efforts typically only consider the author's analyses and supplemental materials from the papers. While understanding the current rate of open science adoption is important, it is also vital that we explore the factors that may encourage such adoption. One such factor may be publishing organizations setting open science requirements for submitted articles: encouraging researchers to adopt more rigorous reporting and research practices. For example, within the education technology discipline, theACM Conference on Learning @ Scale (L@S) has been promoting open science practices since 2018 through a Call For Papers statement. The purpose of this study was to replicate previous papers within the proceedings of L@S and compare the degree of open science adoption and robust reproducibility practices to other conferences in education technology without a statement on open science. Specifically, we examined 93 papers and documented the open science practices used. We then attempted to reproduce the results with invitation from authors to bolster the chance of success. Finally, we compared the overall adoption rates to those from other conferences in education technology. Although the overall responses to the survey were low, our cursory review suggests that researchers at L@S might be more familiar with open science practices compared to the researchers who published in the International Conference on Artificial Intelligence in Education (AIED) and the International Conference on Educational Data Mining (EDM): 13 of 28 AIED and EDM responses were unfamiliar with preregistrations and 7 unfamiliar with preprints, while only 2 of 7 L@S responses were unfamiliar with preregistrations and 0 with preprints. The overall adoption of open science practices at L@S was much lower with only 1% of papers providing open data, 5% providing open materials, and no papers had a preregistration. All openly accessible work can be found in an Open Science Framework project. 

Within the field of education technology, learning analytics has increased in popularity over the past decade. Researchers conduct experiments and develop software, building on each other’s work to create more intricate systems. In parallel, open science — which describes a set of practices to make research more open, transparent, and reproducible — has exploded in recent years, resulting in more open data, code, and materials for researchers to use. However, without prior knowledge of open science, many researchers do not make their datasets, code, and materials openly available, and those that are available are often difficult, if not impossible, to reproduce. The purpose of the current study was to take a close look at our field by examining previous papers within the proceedings of the International Conference on Learning Analytics and Knowledge, and document the rate of open science adoption (e.g., preregistration, open data), as well as how well available data and code could be reproduced. Specifically, we examined 133 research papers, allowing ourselves 15 minutes for each paper to identify open science practices and attempt to reproduce the results according to their provided specifications. Our results showed that less than half of the research adopted standard open science principles, with approximately 5% fully meeting some of the defined principles. Further, we were unable to reproduce any of the papers successfully in the given time period. We conclude by providing recommendations on how to improve the reproducibility of our research as a field moving forward. All openly accessible work can be found in an Open Science Foundation project1. 

As evidence grows supporting the importance of non-cognitive factors in learning, computer-assisted learning platforms increasingly incorporate non-academic interventions to influence student learning and learning related-behaviors. Non-cognitive interventions often attempt to influence students’ mindset, motivation, or metacognitive reflection to impact learning behaviors and outcomes. In the current paper, we analyze data from five experiments, involving seven treatment conditions embedded in mastery-based learning activities hosted on a computer-assisted learning platform focused on middle school mathematics. Each treatment condition embodied a specific non-cognitive theoretical perspective. Over seven school years, 20,472 students participated in the experiments. We estimated the effects of each treatment condition on students’ response time, hint usage, likelihood of mastering knowledge components, learning efficiency, and post-tests performance. Our analyses reveal a mix of both positive and negative treatment effects on student learning behaviors and performance. Few interventions impacted learning as assessed by the post-tests. These findings highlight the difficulty in positively influencing student learning behaviors and outcomes using non-cognitive interventions. 

Prior work analyzing tutoring sessions provided evidence that highly effective tutors, through their interaction with students and their experience, can perceptively recognize incorrect processes or “bugs” when students incorrectly answer problems. Researchers have studied these tutoring interactions examining instructional approaches to address incorrect processes and observed that the format of the feedback can influence learning outcomes. In this work, we recognize the incorrect answers caused by these buggy processes as Common Wrong Answers (CWAs). We examine the ability of teachers and instructional designers to identify CWAs proactively. As teachers and instructional designers deeply understand the common approaches and mistakes students make when solving mathematical problems, we examine the feasibility of proactively identifying CWAs and generating Common Wrong Answer Feedback (CWAFs) as a formative feedback intervention for addressing student learning needs. As such, we analyze CWAFs in three sets of analyses. We first report on the accuracy of the CWAs predicted by the teachers and instructional designers on the problems across two activities. We then measure the effectiveness of the CWAFs using an intent-to-treat analysis. Finally, we explore the existence of personalization effects of the CWAFs for the students working on the two mathematics activities. 

As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user experiences, curriculum structures, and tutoring strategies in order to ensure the effectiveness of their platform and personalize the education of the students using it. These experiments are typically analyzed on an individual basis in order to reveal insights on a specific aspect of students' online educational experience. In this work, the data from 50,752 instances of 30,408 students participating in 50 different experiments conducted at scale within the online learning platform ASSISTments were aggregated and analyzed for consistent trends across experiments. By combining common experimental conditions and normalizing the dependent measures between experiments, this work has identified multiple statistically significant insights on the impact of various skill mastery requirements, strategies for personalization, and methods for tutoring in an online setting. This work can help direct further experimentation and inform the design and improvement of new and existing online learning platforms. The anonymized data compiled for this work are hosted by the Open Science Foundation and can be found at https://osf.io/59shv/. 

As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user experiences, curriculum structures, and tutoring strategies in order to ensure the effectiveness of their platform and personalize the education of the students using it. These experiments are typically analyzed on an individual basis in order to reveal insights on a specific aspect of students’ online educational experience. In this work, the data from 50,752 instances of 30,408 students participating in 50 different experiments conducted at scale within the online learning platform ASSISTments were aggregated and analyzed for consistent trends across experiments. By combining common experimental conditions and normalizing the dependent measures between experiments, this work has identified multiple statistically significant insights on the impact of various skill mastery requirements, strategies for personalization, and methods for tutoring in an online setting. This work can help direct further experimentation and inform the design and improvement of new and existing online learning platforms. The anonymized data compiled for this work are hosted by the Open Science Foundation and can be found at https://osf.io/59shv/. 

As online learning platforms become more ubiquitous through- out various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different meth- ods used to structure online education and tutoring. To- wards this endeavor, some platforms have performed ran- domized controlled experiments to compare different user experiences, curriculum structures, and tutoring strategies in order to ensure the effectiveness of their platform and personalize the education of the students using it. These experiments are typically analyzed on an individual basis in order to reveal insights on a specific aspect of students’ online educational experience. In this work, the data from 50,752 instances of 30,408 students participating in 50 differ- ent experiments conducted at scale within the online learn- ing platform ASSISTments were aggregated and analyzed for consistent trends across experiments. By combining com- mon experimental conditions and normalizing the dependent measures between experiments, this work has identified mul- tiple statistically significant insights on the impact of var- ious skill mastery requirements, strategies for personaliza- tion, and methods for tutoring in an online setting. This work can help direct further experimentation and inform the design and improvement of new and existing online learning platforms. The anonymized data compiled for this work are hosted by the Open Science Foundation and can be found at https://osf.io/59shv/. 

As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user experiences, curriculum structures, and tutoring strategies in order to ensure the effectiveness of their platform and personalize the education of the students using it. These experiments are typically analyzed on an individual basis in order to reveal insights on a specific aspect of students’ online educational experience. In this work, the data from 50,752 instances of 30,408 students participating in 50 different experiments conducted at scale within the online learning platform ASSISTments were aggregated and analyzed for consistent trends across experiments. By combining common experimental conditions and normalizing the dependent measures between experiments, this work has identified multiple statistically significant insights on the impact of various skill mastery requirements, strategies for personalization, and methods for tutoring in an online setting. This work can help direct further experimentation and inform the design and improvement of new and existing online learning platforms. The anonymized data compiled for this work are hosted by the Open Science Foundation and can be found at https://osf.io/59shv/. 

As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user experiences, curriculum structures, and tutoring strategies in order to ensure the effectiveness of their platform and personalize the education of the students using it. These experiments are typically analyzed on an individual basis in order to reveal insights on a specific aspect of students' online educational experience. In this work, the data from 50,752 instances of 30,408 students participating in 50 different experiments conducted at scale within the online learning platform ASSISTments were aggregated and analyzed for consistent trends across experiments. By combining common experimental conditions and normalizing the dependent measures between experiments, this work has identified multiple statistically significant insights on the impact of various skill mastery requirements, strategies for personalization, and methods for tutoring in an online setting. This work can help direct further experimentation and inform the design and improvement of new and existing online learning platforms. The anonymized data compiled for this work are hosted by the Open Science Foundation and can be found at https://osf.io/59shv/.