Geometry Project Descriptions
Robust learning with a Meta-Cognitive Tutor
- Primary Investigator: Vincent Aleven
- Co-PIs & Other Investigator(s): Bruce McLaren
Cluster(s): Interactive Communications
Course(s): Geometry
The goal of our research is to investigate the role of meta-cognitive instruction in student learning, in particular, whether instruction focused on improving students’ help-seeking behavior prepares students for better future learning. We focus on three hypotheses:
Hypothesis 1: a cognitive model of adaptive help-seeking behavior can be used to provide effective tutoring of help-seeking skills by means of a “Help Tutor,” effective in the sense that it leads to better help seeking and better learning, both during and after its use.
Hypothesis 2: declarative instruction on proper use of the tutor’s help facilities has a positive influence on students’ help seeking behavior and learning.
Hypothesis 3: engaging students’ in self-assessment of their own (or their peers’) help-seeking behavior leads to better help-seeking and learning; a cognitive model of help seeking can be of use in automatically selecting, on an individual basis, help-seeking episodes that should be self-assessed (e.g., clear examples of good and bad help-seeking behavior, or examples of so-so help-seeking behavior repeated often by the given student).
These hypotheses will be tested in a series of experiments in the Geometry LearnLab.
Most recent project report:
(2006-03-16) dec2005_helpseeking1.doc
Most recent project poster:
(2006-03-16) help_tutor_poster1.pdf
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
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
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
The Operator Application Reification (OAR) Tutor
- Primary Investigator: Yvonne Kao
- Co-PIs & Other Investigator(s): Ido Roll, Ken Koedinger
Cluster(s): Interactive Communications
Course(s): Geometry
Geometry students often acquire shallow knowledge during the course of learning. Some research suggests that this is due to unproductive learning goals: students focus on generating values rather than learning the deep features of the relevant knowledge component. However, an open question remains with regard to how these goals can be set.
In order to encourage better feature validity, we built the Operation Application Reification (OAR) system, which scaffolds the setting of pedagogically-relevant goals using two means: a goal scaffold that directs students’ attention to the deep features of the skill, and using foils, which poses dilemmas during the application process, thus making students aware of the deep features of the problem.
We hypothesize that the OAR Tutor, built in CTAT, will promote deep conceptual understanding. We further hypothesize that the difficulty students have with complex problems originates in poor feature validity of the basic skills. Therefore, the OAR tutor (which teaches the simple skills) should lead to accelerated future learning of complex problems. These hypotheses will be tested with a Difficulty Factor Analysis (DFA) and two LearnLab studies in Geometry, using existing assessments and tailored online tests.
Most recent project report:
(2006-11-28) roll kao oar.doc
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
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