Coordinative Learning
Deconstructing the Process Benefits of Note-taking
- Primary Investigator: Aaron Bauer
- Co-PIs & Other Investigator(s): Ken Koedinger
Cluster(s): Coordinative Learning, Fluency
Course(s): Unknown
We are investigating the role note-taking plays in encouraging active processing. The process of taking a note has been shown to improve long-term retention of learning material, irregardless of whether the note is reviewed. The source of these process benefits is unclear. Note-taking applications provide the ability to exert a level of control over the note-taking process that was previously unavailable to traditional pencil-and-paper note-taking research. Our project takes advantage of this control to experimentally evaluate note-taking and learning.
Our research currently explores two hypotheses regarding the process benefits of note-taking. First, we believe that note-taking facilitates long-term retention when it encourages students to focus on the critical components of the ideas they are recording. Second, we believe that when note-taking applications require students to generate, rather than passively select, their own notes, they will improve learning. We have designed experiments to evaluate these hypotheses. The results of these experiments will not only result in a further understanding of the note-taking process effect, but will also influence the design of applications that support the retention component of robust learning.
Most recent project report:
(2006-11-28) bauer-notetakingfall06.doc
Most recent project poster:
(2006-05-01) bauer - notetaking2006spring.ppt
A Multimodal Interface for Solving Equations
- Primary Investigator: Lisa Anthony
- Co-PIs & Other Investigator(s): Jie Yang, Ken Koedinger
Cluster(s): Coordinative Learning, Enabling Technology
Course(s): Algebra
The long-term goal of this project is to develop a multimodal intelligent tutoring system that will allow students to use handwriting input to solve mathematics equations online. We believe that providing a handwriting-based interface that is more like paper, more natural and which reduces the cognitive load of the student – who is freed up to think solely about the mathematics and not about how to type math symbols or how to find things in menus – will result in better learning. This project was a one-year project, setting the stage for motivating further research in this area by answering the following questions:
1. From a usability perspective, which input method (keyboard, handwriting, speech, or combinations thereof) is more efficient and/or tends to have less errors?
2. How will students naturally interact multimodally when solving mathematical equations?
3. From a technological perspective, how accurate will the handwriting and speech recognition technologies be with or without the ability to learn from each other and improve over time?
This project has reached the end of its PSLC funding and is in the process of applying for a grant for the upcoming year. During this year, we have explored the above questions through two user studies, one with general users and one with students learning algebra. In both user populations, we found several advantages of using handwriting-based interfaces over traditional keyboard-and-mouse interfaces. Handwriting-based equation entry is both faster and more efficient than keyboard-and-mouse; in addition, we found that users rated handwriting more highly in a post-session questionnaire. We also found some preliminary evidence in support of the following: that people’s input tends to be more variable in some modalities than in others; that people tend to leave out ambiguity control phrases such as “quantity” and “open parentheses” more in speech than actual physical parentheses in handwriting; and that the errors users make in entry tend to be non-overlapping across modalities, meaning that users make errors in different places in the same equation in different modalities. The first two points will likely make recognition of user input more difficult, while the third point may in fact help alleviate this by allowing the use of co-training and co-recognition of simultaneous or redundant input streams. In our second study, we additionally explored the advantages handwriting may have over keyboard input with respect to learning. High school and middle school algebra students came to the lab and solved simple algebraic equations in one of three modalities: handwriting, keyboard, and handwriting-plusspeech. Preliminary analyses show that students experience higher pre-test to post-test gains with handwriting than with keyboard. The study was controlled for number of problems (and concepts addressed) rather than time, so while we saw similar overall learning gains, handwriting students spent about 1/3 less time than keyboard students. Thus the effective gain, if they were given equal time, we hypothesize to be greater for handwriting than for keyboard. The handwriting-plus-speech method was a shadowing method in which students simply spoke what they were writing as they solved the equations, and this was a very low performer in the study. We explored this method due to technological advantages it might provide, but it seems the pedagogical benefits of spoken self-explanations will not be apparent with a shadowing mechanism.
Most recent project report:
(2006-03-16) anthony-report1.pdf
Most recent project poster:
(2006-03-16) anthony-poster1.pdf
Learning to Read Chinese
- Primary Investigator: Charles Perfetti
- Co-PIs & Other Investigator(s): Ying Liu
Cluster(s): Coordinative Learning
Course(s): Chinese
None
Project Runs: 2004-11-01 to 2006-08-30Most recent project report:
None
Most recent project poster:
None
Microgenetic Analyses
- Primary Investigator: Julie Booth
Cluster(s): Coordinative Learning
Course(s): Algebra
Overlapping waves theory (Siegler, 1996) maintains that individuals know and use a variety of strategies which compete with each other for use in any given situation, and that with improved or increased knowledge, good strategies gradually replace ineffective ones. The current project will use microgenetic analyses to examine students’ strategy use while learning to solve algebraic equations with the Algebra Tutor and to determine whether misconceptions in students’ understanding of Algebra is associated with use of ineffective or “buggy” procedures for solving problems; it will also test how conceptual encoding exercises designed to correct these misconceptions might affect students’ strategy use while learning with the Algebra Tutor. The first goal of the project is to identify common buggy strategies that students attempt to use when solving algebraic equations with the Tutor, as well as the conceptual and procedural prerequisites that are necessary for solving these problems correctly. The second goal is to design an intervention intended to provide students with conceptual encoding exercises which map onto the identified prerequisites; this intervention will then be implemented and tested in a variety of LearnLab Algebra I classrooms. By providing students with co-training (or coordinative learning) opportunities using multiple types of instruction (procedural practice and conceptual encoding) during their lessons, we expect to see less use of buggy strategies, greater use of effective strategies, and improvements in their knowledge of algebra concepts. This is expected to lead to better performance on the Tutor problems in the lesson, as well as three types of robust learning: better transfer of the strategies to subsequent test problems, long-term retention of the better strategies for use in later relevant lessons and review exercises, and accelerated future learning in related units later in the course. The usefulness of these conceptual encoding exercises in diverse populations will also be examined.
Project Runs: 2005-09-01 to 2007-08-31Most recent project report:
(2006-11-21) booth project report for 2006 advisory board.doc
Most recent project poster:
(2006-06-05) booth poster for site visit 2006.ppt
Visual Representations in Science Learning
- Primary Investigator: Jodi Davenport
Cluster(s): Coordinative Learning
Course(s): Chemistry
Visual representations, in the forms of diagrams, notation (e.g., equations), graphs and tables are fundamental tools in science instruction and practice. Whether diagrams or notational systems are helpful aids to problem solving depends critically on the content of the visual representation and how learners are able to process the information they contain. Expert/novice studies have demonstrated that different levels of experience with result in differential processing of the same stimuli. However, it is not known how students are able to refine initially shallow understandings into meaningful chemical concepts. The current project seeks to determine when and how the use of multiple representations during instruction and problem solving will lead to robust learning. The initial platform for this course will be the equilibrium and acid/base units in a second semester college chemistry course.
Project Runs: 2005-09-01 to 2007-09-30Most recent project report:
None
Most recent project poster:
None
Knowledge Tracking
- Primary Investigator: Philip Pavlik
Cluster(s): Coordinative Learning, Fluency
Course(s): Algebra
None
Project Runs: 2005-09-01 to 2008-08-31Most recent project report:
None
Most recent project poster:
None
Bridging the gap between comprehension and production in second language learning
- Primary Investigator: Nel de Jong
- Co-PIs & Other Investigator(s): Charles Perfetti, Robert DeKeyser
Cluster(s): Coordinative Learning, Fluency
Course(s): English, French
None
Project Runs: 2005-09-01 to 2006-08-31Most recent project report:
(2006-04-13) report_ndj_april2006.doc
Most recent project poster:
(2006-04-13) pslc-poster_ndj_april06.ppt
An authoring tool that learns domain principles from
- Primary Investigator: Noboru Matsuda
- Co-PIs & Other Investigator(s): William Cohen, Ken Koedinger
Cluster(s): Coordinative Learning, Enabling Technology
Course(s): Algebra
None
Project Runs: 2004-11-01 to 2006-10-31Most recent project report:
None
Most recent project poster:
None
Contiguity in the classroom: A test of Mayer’s principle
- Primary Investigator: Vincent Aleven
Cluster(s): Coordinative Learning
Course(s): Geometry
This research focuses on robust learning in problem solving with visual (diagram) and verbal (text) representations. We submit that in such a domain, important robust learning processes are tied both to foundational skill building (that is, refining students’ knowledge and its accessibility in varied problem settings) and to sense making (specifically, using visual and verbal knowledge components to reason and to generate explanations of that knowledge). The goals of this research are to investigate how coordination between and integration of visual and verbal information influences robust learning processes, as measured by knowledge transfer and potential for future learning. The Geometry Cognitive Tutor will be used as research vehicle for these studies.
Project Runs: 2005-09-01 to 2006-08-31Most recent project report:
(2006-03-16) contiguityprojectdescr-v21.doc
Most recent project poster:
(2006-03-16) contiguityposter_binder-size.pdf
ESL student self-correction of student-recorded speaking activities
- Primary Investigator: Dawn McCormick
- Co-PIs & Other Investigator(s): Claire Siskin
Cluster(s): Coordinative Learning
Course(s): English
None
Project Runs: 2005-09-01 to 2006-08-31Most recent project report:
(2006-04-20) nsfvisiteslspeakingreport.doc
Most recent project poster:
(2006-03-16) esl speaking ab poster2.ppt
Does learning from examples improve tutored problem solving?
- Primary Investigator: Alexander Renkl
- Co-PIs & Other Investigator(s): Vincent Aleven, Ron Salden
Cluster(s): Coordinative Learning
Course(s): Geometry
Problem solving supported by Cognitive Tutors has been shown to be successful in fostering initial acquisition. However, we believe the addition of example-based learning provides for better sense making early on in the learning process, resulting in better foundational skills.
Consequently, by fading the steps in the examples towards problem solving the knowledge events are refined. Furthermore, the use of self-explanations throughout the entire fading process will increase feature validity.
The students are likely to obtain more robust learning because the fading procedure takes the gradually increasing number of knowledge components into account. Especially, we will address the following main hypotheses:
<P>
Hypothesis 1: A combination of example study and tutored problem solving is better than tutored problem solving alone. As state-of-the-art implementation of example-based learning, faded worked-out examples in which gradually problem-solving demands are introduced are employed. The fading procedure used is a fixed (i.e., non-individualized) method for fading the examples.
<P>Hypothesis 2: When examples are combined with tutored problem solving, an adaptive individualized fading method based on the quality of students’ menu-based answers to self-explanation prompts, is better than a fixed fading method.
<P>Hypothesis 3: Adaptive fading of examples based on an analysis of students’ free-form (natural language) explanations of examples is better than adaptive fading based on explanation entered via menus.
<P>Hypothesis 4: A combination of examples and tutored problem solving is more effective when the previous self-explained examples are accessible during problem solving.
<P>We address these research questions by preparatory lab experiments and subsequent in vivo experiments in the Geometry LearnLab (see Table 1). To prepare for the experiments, the problem sets of the relevant units of the Geometry Cognitive Tutor (the Circles and Angles units) are being analyzed and modified/extended so they are suitable for use with a systematic example-fading procedure.
Most recent project report:
(2006-11-30) examples project report ab meeting december 2006.doc
Most recent project poster:
(2006-05-04) poster-examples-geometry-project-04-17-2006.ppt
Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoichiometry
- Primary Investigator: Bruce McLaren
- Co-PIs & Other Investigator(s): David Yaron, Ken Koedinger
Cluster(s): Coordinative Learning
Course(s): Chemistry
In this study, conducted within the PSLC Chemistry LearnLab, we will investigate whether personalized instructional materials and worked examples can improve learning. The study will involve online (i.e., web-based) learning of stoichiometry, the basic math required to solve many chemistry problems, and will use intelligent tutoring systems developed with the aid of the Cognitive Tutor Authoring Tools (CTAT), a key enabling technology of the PSLC. The study will be piloted at CMU and the University of Pittsburgh and will be executed in full with students enrolled in an Intro to Chemistry class at the University of British Columbia (UBC).
In a recent book by Clark and Mayer (2003), a number of "e-Learning" principles were proposed as guidelines for building e-Learning systems. All of these principles are supported by research in psychology and cognitive science. In the PSLC study we propose, we intend to experiment with two of those principles, both individually and in combination. In particular, we intend to explore:
Personalization Principle One: Use Conversational Rather than Formal Style (Clark and Mayer, 2003, pg. 133-138)
Worked Example Principle One: Replace Some Practice Problems with Worked Examples (Clark and Mayer, 2003, pg. 177- 180)
Most recent project report:
(2006-11-21) stoichstudy-2006-12.doc
Most recent project poster:
(2006-04-20) pslcstoichstudy2006-04.ppt
Learning with Diagrams in Geometry: Strategic Support for Robust Learning
- Primary Investigator: Vincent Aleven
- Co-PIs & Other Investigator(s): Kirsten Butcher
Cluster(s): Coordinative Learning
Course(s): Geometry
None
Project Runs: 2005-08-01 to 2007-10-30Most recent project report:
None
Most recent project poster:
None
Bridging Principles and Examples through Analogy and Explanation
- Primary Investigator: Timothy Nokes
- Co-PIs & Other Investigator(s): Kurt VanLehn
Cluster(s): Coordinative Learning, Fluency, Refinement
Course(s): Physics
None
Project Runs: 2007-01-01 to 2008-06-01Most recent project report:
None
Most recent project poster:
None
Building Social Intelligence in Computer Based Tutors
- Primary Investigator: Richard Mayer
- Co-PIs & Other Investigator(s): Bruce McLaren
Cluster(s): Coordinative Learning
Course(s): Chemistry
The goal of this project is to examine how student learning is affected by social cues in computer-based learning environments, such as the conversational style of online cognitive tutors. In particular, students will learn how to solve stoichiometry problems in the Chemistry LearnLab, using a cognitive tutor that provides hints and feedback in direct style or in polite style (McLaren, Lim, Yaron, & Koedinger, 2007). The stoichiometry tutor has been used for other PSLC studies, in particular those by McLaren et al that have investigated personalization, politeness, and worked examples.
Our study is based on Brown and Levinson’s (1987) theory of politeness, which specifies how people create polite requests; Reeves and Nass’ (1996, 2005) media equation theory, which specifies the conditions under which people accept computers as conversational partners; and Mayer’s (2005) personalization principle in which people work harder to learn when they feel they are in a conversation with a tutor. Our working hypothesis is that learners work harder to make sense of lessons when they work with polite rather than direct tutors, because learners are more likely to accept polite tutors as conversational partners (Mayer, 2005; Wang, Johnson, Mayer, Rizzo, Shaw, & Collins, 2008).
Most recent project report:
None
Most recent project poster:
None
Providing Optimal Support for Robust Learning of Syntactic Constructions in ESL
- Primary Investigator: Lori Levin
- Co-PIs & Other Investigator(s): GwenF Frishkoff, Nel de Jong, Philip Pavlik
Cluster(s): Coordinative Learning, Fluency, Refinement
Course(s): English
This project addresses a problem of language use in second language learners, the appropriate use of a syntactic construction in response to subtle features of the communicative situation. In some cases, using a syntactic construction may be straightforward: use an imperative (Drop it!) to issue a command; use You should…to give advice, etc. In other cases, use of a syntactic construction may not be mastered even by advanced students. Use of the articles the and a, and use of tenses like the present perfect tense (They have lived here for two years) fall into this category. This project focuses on the use of the dative alternation (I gave him a book/I gave a book to him) by ESL students. Bresnan et al. (2005) have shown that the dative alternation is sensitive to fourteen features of the discourse situation and have constructed a linear regression model of the dative alternation for native speakers. Sensitivity to fourteen features with different weights obviously cannot be taught explicitly. However, our hypothesis is that a model of this type can be used as a basis for instruction if the right examples are presented at the right times with the right frequencies. The project includes two studies, one to calibrate the student model and one to test the hypothesis. The model of native speaker dative alternation will be integrated with the Pavlik and Anderson (2005) model in order to create a model of how the dative alternation is learned. The result of this project will be a framework (theory, formal model, and tools) that can be applied to the study of other hard-to-use constructions.
Project Runs: 2006-08-01 to 2007-07-31Most recent project report:
(2006-11-23) project-report-template[ll].doc
Most recent project poster:
None
Extension of “Learning to read Chinese”
- Primary Investigator: Ying Liu
- Co-PIs & Other Investigator(s): Charles Perfetti, Min Wang, Tom Mitchell
Cluster(s): Coordinative Learning
Course(s): Unknown
None
Project Runs: 2006-09-01 to 2006-12-31Most recent project report:
None
Most recent project poster:
None
Enhancing Learning Through Computer Animation
- Primary Investigator: Stephen Reed
- Co-PIs & Other Investigator(s): Bob Hoffman, Albert Corbett
Cluster(s): Coordinative Learning
Course(s): Algebra
None
Project Runs: 2007-07-01 to 2008-06-30Most recent project report:
None
Most recent project poster:
None
How Content and Interface Features Influence Student Choices Within the Learning Space
- Primary Investigator: Ryan Baker
- Co-PIs & Other Investigator(s): Albert Corbett, Ken Koedinger
Cluster(s): Coordinative Learning, Enabling Technology, PSLC Course
Course(s): Algebra, Geometry
None
Project Runs: 2007-07-15 to 2008-09-30Most recent project report:
None
Most recent project poster:
None
Improving Skill at Solving Equations via Better Encoding of Algebraic Concepts
- Primary Investigator: Julie Booth
- Co-PIs & Other Investigator(s): Robert Siegler, Ken Koedinger, Bethany Rittle-Johnson
Cluster(s): Coordinative Learning
Course(s): Algebra
None
Project Runs: 2007-09-01 to 2008-08-31Most recent project report:
None
Most recent project poster:
None
LABGEBRA: Deciphering Invention as Preparation for Future
- Primary Investigator: Ido Roll
- Co-PIs & Other Investigator(s): Vincent Aleven, Ken Koedinger, Daniel Schwartz
Cluster(s): Coordinative Learning
Course(s): Algebra
None
Project Runs: 2007-09-01 to 2008-08-31Most recent project report:
None
Most recent project poster:
None
Exploring the Assistance Dilemma and Robust Learning in the
- Primary Investigator: Bruce McLaren
- Co-PIs & Other Investigator(s): Ken Koedinger, David Yaron
Cluster(s): Coordinative Learning
Course(s): Chemistry
We propose to conduct two new in vivo studies with the stoichiometry tutor in order to help the PSLC, and the learning science community at large, better understand the Assistance Dilemma. The body of “assistance” research till now suggests that learning benefits conform to an inverted-U shaped curve in which lesser assistance approaches, such as untutored problem solving, and greater assistance approaches, such as students studying and self explaining explained examples, lead to lesser learning benefits, while combinations of these approaches, in particular, alternating worked examples with problem solving, lead to greater learning benefits. We will conduct one study that has not been done before – comparing alternating worked examples and tutored problem solving with all worked examples and all tutored problem solving – to provide a new data point in exploring whether the hypothesized inverted-U shaped curve is a reality (or not). We will also conduct a study to investigate whether a lesser assistance approach, i.e., students studying and self explaining unexplained examples, leads to a better robust learning outcome than a greater assistance approach, i.e., students studying and self explaining explained examples. Both of our studies will include robust learning measures, specifically transfer and long-term retention. We will also perform data mining and knowledge component (KC) analysis of both pre-existing study data and new study data to better understand why we are getting the results we’ve gotten so far. For instance, we hypothesize that students may be turning tutored problems into worked examples by drilling down to the bottom-out hints. Such a phenomenon could explain why the alternating worked examples / tutored problem solving approach has not led to more learning than tutored problem solving alone in our first three stoichiometry studies. We will test this and additional hypotheses regarding our results.
Project Runs: 2008-01-01 to 2008-05-31Most recent project report:
None
Most recent project poster:
None
Concept Development in Chemistry: Coordinating
- Primary Investigator: Jodi Davenport
- Co-PIs & Other Investigator(s): David Klahr, Ken Koedinger
Cluster(s): Coordinative Learning
Course(s): Chemistry
None
Project Runs: 2007-10-01 to 2008-09-30Most recent project report:
None
Most recent project poster:
None
Using Comparisons to Leverage Learning from Chemistry
- Primary Investigator: Norma Chang
Cluster(s): Coordinative Learning, Refinement
Course(s): Chemistry
None
Project Runs: 2007-09-01 to 2009-08-31Most recent project report:
None
Most recent project poster:
None
Generating Visual-Verbal Connections to Promote Robust Learning in Geometry
- Primary Investigator: Kirsten Butcher
- Co-PIs & Other Investigator(s): Vincent Aleven
Cluster(s): Coordinative Learning
Course(s): Geometry
None
Project Runs: 2007-09-01 to 2008-08-31Most recent project report:
None
Most recent project poster:
None
Towards a Theory of Learning Errors: Application of a Synthetic Student to Analyze Errors learned by Human Students
- Primary Investigator: Noboru Matsuda
- Co-PIs & Other Investigator(s): William Cohen, Ken Koedinger
Cluster(s): Coordinative Learning, Enabling Technology
Course(s): Algebra
None
Project Runs: 2008-09-01 to 2009-08-31Most recent project report:
None
Most recent project poster:
None
Analogical Scaffolding in Collaborative Learning
- Primary Investigator: Timothy Nokes
- Co-PIs & Other Investigator(s): Soniya Gadgil
Cluster(s): Coordinative Learning, Interactive Communications
Course(s): Physics
Past research has shown that collaboration can enhance learning in certain conditions. However, not much work has explored the cognitive mechanisms that underlie such learning. Chi,
Hausmann and Roy (2004) propose three mechanisms including: self-explaining, other-directed
explaining, and co-construction. In the current study, we will examine the use of these
mechanisms when participants learn from worked examples across different collaborative
contexts. We compare the effects of adding prompts that encourage analogical comparison to
prompts that focus on single examples (non-comparison) to a traditional instruction condition, as students learn to solve Physics problems in the domain of rotational kinematics. Students learning processes will be analyzed by examining their verbal protocols. Learning will be
assessed via robust measures such as long-term retention and transfer.
Most recent project report:
None
Most recent project poster:
None