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Adam C. Sales; Ethan Prihar; Johann Gagnon-Bartsch; Ashish Gurung; Neil T. Heffernan – Grantee Submission, 2022
Randomized A/B tests allow causal estimation without confounding but are often under-powered. This paper uses a new dataset, including over 250 randomized comparisons conducted in an online learning platform, to illustrate a method combining data from A/B tests with log data from users who were not in the experiment. Inference remains exact and…
Descriptors: Research Methodology, Educational Experiments, Causal Models, Computation
April Murphy; Steve Ritter – Grantee Submission, 2022
Large-scale, classroom-based experiments using adaptive instructional software pose somewhat unique challenges for experimental design and deployment. One reason for this is that adaptive software allows students to advance through the curriculum at different rates and encounter content at different times, meaning that content targeted for…
Descriptors: Educational Experiments, Assistive Technology, Computer Software, Computer Assisted Instruction
Ethan Prihar; Manaal Syed; Korinn Ostrow; Stacy Shaw; Adam Sales; Neil Heffernan – Grantee Submission, 2022
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…
Descriptors: Educational Trends, Electronic Learning, Educational Experience, Educational Experiments
Julia Cambre; Ying Liu; Rebecca E. Taylor; Chinmay Kulkarni – Grantee Submission, 2019
This paper investigates whether voice assistants can play a useful role in the specialized work-life of the knowledge worker (in a biology lab). It is motivated both by promising advances in voice-input technology, and a long-standing vision in the community to augment scientific processes with voice-based agents. Through a reflection on our…
Descriptors: Assistive Technology, Artificial Intelligence, Laboratory Equipment, Scientists
Li, Haiying; Gobert, Janice; Dickler, Rachel – Grantee Submission, 2018
Science assessments should evaluate the full complement of inquiry practices (NGSS, 2013). Our previous work has shown that a large proportion of students' open responses did not match their scientific investigations (Li et al., 2017a). The present study both unpacks and compares the sub-components underlying students' performance for…
Descriptors: Inquiry, Intelligent Tutoring Systems, Middle School Students, Content Area Writing
Allen, Laura K.; Mills, Caitlin; Perret, Cecile; McNamara, Danielle S. – Grantee Submission, 2019
This study examines the extent to which instructions to self-explain vs. "other"-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146)…
Descriptors: Language Processing, Science Instruction, Computational Linguistics, Teaching Methods
Selent, Douglas; Patikorn, Thanaporn; Heffernan, Neil – Grantee Submission, 2016
In this paper, we present a dataset consisting of data generated from 22 previously and currently running randomized controlled experiments inside the ASSISTments online learning platform. This dataset provides data mining opportunities for researchers to analyze ASSISTments data in a convenient format across multiple experiments at the same time.…
Descriptors: Intelligent Tutoring Systems, Data, Randomized Controlled Trials, Electronic Learning
Li, Haiying; Gobert, Janice; Dickler, Rachel – Grantee Submission, 2017
Researchers are trying to develop assessments for inquiry practices to elicit students' deep science learning, but few studies have examined the relationship between students' "doing," i.e. "performance assessment," and "writing," i.e. "open responses," during inquiry. Inquiry practices include generating…
Descriptors: Inquiry, Science Instruction, Science Experiments, Writing (Composition)
Sao Pedro, Michael A.; Gobert, Janice D.; Betts, Cameron G. – Grantee Submission, 2014
There are well-acknowledged challenges to scaling computerized performance-based assessments. One such challenge is reliably and validly identifying ill-defined skills. We describe an approach that leverages a data mining framework to build and validate a detector that evaluates an ill-defined inquiry process skill, designing controlled…
Descriptors: Performance Based Assessment, Computer Assisted Testing, Inquiry, Science Process Skills
Sao Pedro, Michael A.; Baker, Ryan S. J. d.; Gobert, Janice D. – Grantee Submission, 2013
When validating assessment models built with data mining, generalization is typically tested at the student-level, where models are tested on new students. This approach, though, may fail to find cases where model performance suffers if other aspects of those cases relevant to prediction are not well represented. We explore this here by testing if…
Descriptors: Educational Research, Data Collection, Data Analysis, Generalizability Theory
Gobert, Janice Darlene; Sao Pedro, Michael A.; Baker, Ryan S. – Grantee Submission, 2012
In this paper we explored whether engaging in two inquiry skills associated with data collection, designing controlled experiments and testing stated hypotheses, within microworlds for one physical science domain (density) impacted the acquisition of inquiry skills in another domain (phase change). To do so, we leveraged educational data mining…
Descriptors: Data Collection, Learning Analytics, Inquiry, Science Process Skills