I am a researcher and consultant in cognitive science and online education.I do interdisciplinary research on general learning principles to support the practical development of digital online educational resources that can also complement in-person & blended learning.
I focus on the collaborative development of modular online resources or "MOOClets" that are targeted at a practical goal, as well as designed to support randomized experiments and other research to iteratively improve their learning benefits for students.
My academic work consists of cognitive science research to understand how people learn – through experiments or A/B tests, construction of assessments, and statistical modeling. My practical work consists of consulting to improve and evaluate learning from online educational resources.
Examples include increasing motivation and reflective problem-solving while solving mathematics exercises on Khan Academy, strategies for self-questioning that enhance learning from videos in MOOCs, and digital tools to provide in-the-moment guidance to students in applying management concepts from courses to everyday interactions with people. You can contact me at joseph_jay_williams AT harvard DOT edu.
In this blend of scientific research and applications I collaborate and consult with domain/topic experts, instructors, designers, and researchers from diverse disciplines like education, psychology, human-computer interaction and machine learning. I draw on theories and methodology from research I have done, as well as synthesizing findings from other scientists, work on behavior change, reviews of evidence-based best practices for teaching and learning, practical experience as a statistics tutor, evaluations of educational technology products & authoring tools for e-learning, and experience as an ed-tech consultant and science & technology advisor.
I am currently a Research Fellow at HarvardX, the online learning research and development component at Harvard. I did a postdoc at Stanford University in the Graduate School of Education and Lytics Lab. I received my PhD from UC Berkeley's Psychology Department in Experimental and Computational Cognitive Science. I worked with Tania Lombrozo on laboratory experiments that investigated why prompting people to explain "why?" promotes their learning, and proposing the novel Subsumptive Constraints Account of explanation and learning. I worked with Tom Griffiths on using Bayesian statistics and methods from machine learning to develop probabilistic models of how people make judgments about randomness and explanation, and learn about causes, categories, and functions.
My work on online educational resources allows me to combine basic cognitive science research – using experiments and computational modeling to understand how people learn – with applied research that improves education for real students and designs evidence-based educational web-applications. I focus on how answering questions and generating explanations guides people's learning, and applying learning principles from cognitive science, such as how adding question/explanation prompts to online videos & interactive exercises enhances understanding and helps people acquire new learning strategies. I am currently running studies that investigate how to promote students' construction of explanations when solving mathematics exercises on www.khanacademy.org (here is an example of a prototype), how to measurably increase students' motivation and grades by improving web lessons that teach students that intelligence is malleable, and how to change metacognitive habits and behaviors in MOOCs through Electronic Performance Support Tools.
My technology enthusiasms include documenting useful software – for online research, collaboration, and knowledge management – and reviewing and teaching people about online education software and programming skills that support rapid authoring of pedagogically sophisticated instruction containing "in vivo" experiments. I also do ed-tech entrepreneurship and consulting, drawing on my own research, synthesizing and applying a range of work by other academics, and using the Internet and online educational products to disseminate scientific insights about effective pedagogy and technology for public both education and industry.
Below is a brief summary of my research, with more information on the research overview page. This website also has pages with my Academic Papers, my CV and brief Resume, my General Talks about applying cognitive science to improve online education, and the Research-Based Learning Principles that form the foundation for these talks – a hyperlinked list of papers synthesized from the literature on cognitive science and education. Feel free to contact me with questions or suggestions at joseph_jay_williams AT harvard DOT edu.
Online Education Research
Bridging laboratory research and actual practice has always been challenging, but online education presents an unprecedented context in which to do this. Carrying out experiments in the context of online education can support rigorous research, because it allows substantial experimental control – random assignment to conditions, precise specification of what is manipulated, and quantifiable measures of learning. At the same time, research that explores learning processes with an eye toward their enhancement possesses a great deal of ecological validity, and permits iterative improvement of online educational environments. Instead of doing lab experiments and then following a costly process to extend the results to a physical classroom, online education allows for in vivo studies: Experimental and control conditions are simply different instructional strategies, stimuli are educational materials students use every day, and dependent measures are formal assessments.
Explanation & Learning Research
Generating explanations has been shown to improve learning (e.g. Fonseca & Chi, 2011), and has great promise as a tool in online education. Instructors provide guidance through the questions they ask, while learners still construct the knowledge themselves, and can learn even without feedback. Beyond educational settings, people constantly wonder "why?", such as why objects and people belong to certain categories or why others behave the way they do. Generating and evaluating explanations guides an individual's causal reasoning, categorization, and property induction, promotes learning and transfer in educational settings, and drives conceptual development in children.
My research has proposed a subsumptive constraints account: Explaining "why?" drives people to seek underlying generalizations, understanding how the fact or observation being explained could be anticipated as an instance of a broader pattern. For example, explaining why 2 x 6 = 12 invokes the principle that multiplication is repeated addition, and an explanation like "John is a teacher because he's a caring person" appeals to a regularity – that caring people are more likely to become teachers. I have evidence for four predictions of this account: 1. Counterintuitively, explanation's subsumptive constraint can impair learning when it promotes the use of misleading patterns (Williams, Lombrozo, & Rehder, 2013). 2. Seeking explanations does not simply boost pattern discovery, but particularly promotes the discovery of broad, unifying patterns that account for a range of facts (Williams & Lombrozo, 2010). 3. Explaining increases learners' consultation of their prior knowledge to identify and privilege those patterns that prior beliefs suggest are likely to generalize to novel contexts (Williams & Lombrozo, 2013). 4. Explaining is at times necessary for learning from anomalous observations (that conflict with prior beliefs), as it drives people towards broader generalizations (Williams, Walker, & Lombrozo, 2012). These results have been extended to children as young as five (Walker, Williams, Lombrozo & Gopnik, 2012).