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.

Solving mathematical problems is cognitively complex, involving strategy formulation, solution development, and the application of learned concepts. However, gaps in students' knowledge or weakly grasped concepts can lead to errors. Teachers play a crucial role in predicting and addressing these difficulties, which directly influence learning outcomes. However, preemptively identifying misconceptions leading to errors can be challenging. This study leverages historical data to assist teachers in recognizing common errors and addressing gaps in knowledge through feedback. We present a longitudinal analysis of incorrect answers from the 2015-2020 academic years on two curricula, Illustrative Math and EngageNY, for grades 6, 7, and 8. We find consistent errors across 5 years despite varying student and teacher populations. Based on these Common Wrong Answers (CWAs), we designed a crowdsourcing platform for teachers to provide Common Wrong Answer Feedback (CWAF). This paper reports on an in vivo randomized study testing the effectiveness of CWAFs in two scenarios: next-problem-correctness within-skill and next-problem-correctness within-assignment, regardless of the skill. We find that receiving CWAF leads to a significant increase in correctness for consecutive problems within-skill. However, the effect was not significant for all consecutive problems within-assignment, irrespective of the associated skill. This paper investigates the potential of scalable approaches in identifying Common Wrong Answers (CWAs) and how the use of crowdsourced CWAFs can enhance student learning through remediation.

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 ([email protected]) 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 [email protected] 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 [email protected] might be more familiar with open science practices compared to the researchers who published in theInternational Conference on Artificial Intelligence in Education (AIED) and theInternational 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 [email protected] responses were unfamiliar with preregistrations and 0 with preprints. The overall adoption of open science practices at [email protected] was much lower with only 1% of papers providing open data, 5% providing open materials, and no papers had a preregistration.

Across the past decade, open science has increased in momentum, making research more openly available and reproducible. In addition, learning at scale systems have been developed to collect and apply models, features and reports to better support students and teachers towards their goals. In this tutorial, we will provide an overview of open science practices and their benefits and mitigation within research. In the second part of this tutorial, we will use the Open Science Framework to make, collaborate, and share projects - demonstrating how to make materials, code, and data open. The final part of this tutorial will go over some mitigation strategies when releasing datasets and materials so other researchers may easily reproduce them. Participants in this tutorial learn what the practices of open science are, how to use them in their own research, and how to use the Open Science Framework.

Many online learning platforms and MOOCs incorporate some amount of video-based content into their platform, but there are few randomized controlled experiments that evaluate the effectiveness of the different methods of video integration. Given the large amount of publicly available educational videos, an investigation into this content's impact on students could help lead to more effective and accessible video integration within learning platforms. In this work, a new feature was added into an existing online learning platform that allowed students to request skill-related videos while completing their online middle-school mathematics assignments. A total of 18,535 students participated in two large-scale randomized controlled experiments related to providing students with publicly available educational videos. The first experiment investigated the effect of providing students with the opportunity to request these videos, and the second experiment investigated the effect of using a multi-armed bandit algorithm to recommend relevant videos. Additionally, this work investigated which features of the videos were significantly predictive of students' performance and which features could be used to personalize students' learning. Ultimately, students were mostly disinterested in the skill-related videos, preferring instead to use the platforms existing problem-specific support, and there was no statistically significant findings in either experiment. Additionally, while no video features were significantly predictive of students' performance, two video features had significant qualitative interactions with students' prior knowledge, which showed that different content creators were more effective for different groups of students. These findings can be used to inform the design of future video-based features within online learning platforms and the creation of different educational videos specifically targeting higher or lower knowledge students. The data and code used in this work can be found at https://osf.io/cxkzf/.

Personalized learning stems from the idea that students benefit from instructional material tailored to their needs. Many online learning platforms purport to implement some form of personalized learning, often through on-demand tutoring or self-paced instruction, but to our knowledge none have a way to automatically explore for specific opportunities to personalize students' education nor a transparent way to identify the effects of personalization on specific groups of students. In this work we present the Automatic Personalized Learning Service (APLS). The APLS uses multi-armed bandit algorithms to recommend the most effective support to each student that requests assistance when completing their online work, and is currently used by ASSISTments, an online learning platform. The first empirical study of the APLS found that Beta-Bernoulli Thompson Sampling, a popular and effective multi-armed bandit algorithm, was only slightly more capable of selecting helpful support than randomly selecting from the relevant support options. Therefore, we also present Decision Tree Thompson Sampling (DTTS), a novel contextual multi-armed bandit algorithm that integrates the transparency and interpretability of decision trees into Thomson sampling. In simulation, DTTS overcame the challenges of recommending support within an online learning platform and was able to increase students' learning by as much as 10% more than the current algorithm used by the APLS. We demonstrate that DTTS is able to identify qualitative interactions that not only help determine the most effective support for students, but that also generalize well to new students, problems, and support content. The APLS using DTTS is now being deployed at scale within ASSISTments and is a promising tool for all educational learning platforms.