Mitchell Nathan
Visit http://website.education.wisc.edu/~mnathan/
How do we represent knowledge and make meaning? What is special about abstract knowledge and representations? I examine these questions through research on STEM learning and instruction from a Learning Sciences perspective, drawing on embodied, social-cognitive, and developmental theories and research methods.
I am currently a Professor of Learning Sciences (Educational Psychology Dept.) at the University of Wisconsin-Madison, Director of the Center on Education and Work, and Director of the IES Postdoctoral Fellowship Program in Mathematical Thinking, Learning and Instruction. I hold faculty appointments in the Department of Curriculum and Instruction, the Psychology Department, and the Wisconsin Center for Educational Research (WCER). I am also a member of the UW Cognitive Science Cluster and the Delta Program steering committee. I served as Chair of the Learning Sciences program from 2004 - 2010.
Address: 1025 West Johnson St, Madison, WI 53706
http://website.education.wisc.edu/mnathan/
How do we represent knowledge and make meaning? What is special about abstract knowledge and representations? I examine these questions through research on STEM learning and instruction from a Learning Sciences perspective, drawing on embodied, social-cognitive, and developmental theories and research methods.
I am currently a Professor of Learning Sciences (Educational Psychology Dept.) at the University of Wisconsin-Madison, Director of the Center on Education and Work, and Director of the IES Postdoctoral Fellowship Program in Mathematical Thinking, Learning and Instruction. I hold faculty appointments in the Department of Curriculum and Instruction, the Psychology Department, and the Wisconsin Center for Educational Research (WCER). I am also a member of the UW Cognitive Science Cluster and the Delta Program steering committee. I served as Chair of the Learning Sciences program from 2004 - 2010.
Address: 1025 West Johnson St, Madison, WI 53706
http://website.education.wisc.edu/mnathan/
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Papers by Mitchell Nathan
found few connections between traditional measures of text readability and performance on story
problems. We hypothesized that recently developed measures of readability and topic incidence measured
by text-mining tools may illuminate associations between text difficulty and problem-solving
measures. We used data from 3,216 middle and high school students from 10 schools using the Cognitive
Tutor Algebra program; these schools were geographically, socioeconomically, racially, and ethnically
diverse. We found that several indicators of the readability and topic of story problems were associated
with students’ tendency to give correct answers and request hints in Cognitive Tutor. We further
examined the individual skill of writing an algebraic expression from a story scenario, and examined
students at the lowest performing schools in the sample only, and found additional associations for these
subsets. Key readability and topic categories that were related to problem-solving measures included
word difficulty, text length, pronoun use, sentence similarity, and topic familiarity. These findings are
discussed in the context of models of mathematics story problem solving and previous research on text
comprehension.
Keywords: readability, algebra, data-mining, intelligent tutoring system, topic interest
found few connections between traditional measures of text readability and performance on story
problems. We hypothesized that recently developed measures of readability and topic incidence measured
by text-mining tools may illuminate associations between text difficulty and problem-solving
measures. We used data from 3,216 middle and high school students from 10 schools using the Cognitive
Tutor Algebra program; these schools were geographically, socioeconomically, racially, and ethnically
diverse. We found that several indicators of the readability and topic of story problems were associated
with students’ tendency to give correct answers and request hints in Cognitive Tutor. We further
examined the individual skill of writing an algebraic expression from a story scenario, and examined
students at the lowest performing schools in the sample only, and found additional associations for these
subsets. Key readability and topic categories that were related to problem-solving measures included
word difficulty, text length, pronoun use, sentence similarity, and topic familiarity. These findings are
discussed in the context of models of mathematics story problem solving and previous research on text
comprehension.
Keywords: readability, algebra, data-mining, intelligent tutoring system, topic interest
The purpose of our study is to understand why diagrams increase learning from lessons. To address this issue, we randomly assigned undergraduates (N = 36) to read a probability lesson either with or without diagrams, while their eye movements were recorded. Students whose lessons included diagrams solved more probability problems correctly at post-test than did students whose lessons did not include diagrams. Students whose lessons included diagrams also had smaller average pupil size and spent less time reading the text than did students whose lessons did not include diagrams. Pupil size and reading times typically increase with task difficulty (Rayner, 1997; van Gog et al., 2009); therefore, this finding indicates that the diagrams lessened the difficulty of reading the lesson. In addition, students whose lessons included diagrams frequently looked to and from the diagram and the text. Their looks to and from the diagram and text may indicate that they were integrating the visual and verbal representations in the lesson (Mason, Tornatora, & Pluchino, 2012).
These findings indicate that both of the previously proposed reasons may explain why students whose lessons included diagrams answered more problems correctly than did students whose lessons did not have diagrams. One is that the diagrams made the lesson text easier to understand; therefore, students could focus their efforts on extracting the content of the lesson, rather than working to comprehend the text. The other is that diagrams encourage students to make connections within the lesson material, which prompts deeper comprehension. These findings enrich our understanding of the benefits of visual representations.