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The Help Seeing Support Environment
Off-Task Behavior
Operator Application Reification
The Role of Invention
While direct instruction and practice have been shown to effective means, students often acquire shallow knowledge and lack robust understanding. Discovery learning, on the other hand, while being engaging, benefits only a small subset of the students (namely, those who manage to discover). Some evidence suggests that there is a win-win solution: invention, prior to instruction and practice, has been shown to accelerate future learning, compared with instruction and practice alone (Schwartz & Martin, 2004), and to be more effective than unstructured discovery (Klahr & Nigam, 2004)During the invention process students are asked to (i) invent a method to reconcile contrasting cases, (ii) assess their success, and (iii) debug their method. Following the invention students receive instruction and practice the canonical solution. This is shown to assist all students. Productive invention does not require successful invention - also students who fail to invent correctly benefited from the invention process.
We hypothesize that students’ own inventions, together with subsequent instruction, are sources for coordinative learning. By attempting to create a model that correctly distinguishes the “contrasting cases” (carefully selected instances within a class of problems) students notice (and to some degree invent) the problem features that an adequate model must take into account, and they attend to them during subsequent instruction. However, alternative explanations for the success of the invention process are possible, with different instructional implications. A “debugging hypothesis” suggests that evaluation and debugging of pre-designed models are sufficient to promote future learning by directing students’ attention to the short-comings of the designed models, and thus to the deep features of the domain. Alternatively, an “unfinished goals” hypothesis suggests that the effect is caused by students reaching impasses during invention. According to this hypothesis, calculation-evaluation are sufficient for preparing for future learning.
We investigate this in a series of ablation studies with the goal of better defining the invention process and identifying the cognitive processes involved. We evaluate both a paper-and-pencil version of the invention, as well as a Cognitive Tutor based intelligent learning environment that facilitates the process. This allows us to better operationalize the process, do a micro-genetic analysis of it, and identify productive patterns of learning trajectories using log mining.
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The Help Seeing Support Environment
While working with a tutoring system, students are expected to regulate their own learning process. However, often, they demonstrate inadequate metacognitive process in doing so. For example, students often ask for help too frequently or not frequently enough.In this project we investigate whether cognitive learning principles can be applied in teaching metacognitive skills. More specifically, we focus on improving students' help-seeking skills. We built the Help Seeking Support Environment, which includes the following components:
1. Direct help seeking instruction using a video, given by the teacher
2. A Self-Assessment Tutor, to help students evaluate their own need for help
3. The Help Tutor - a domain-independent agent that can be added as an adjunct to a cognitive tutor. Rather than making help-seeking decisions for the students, the Help Tutor teaches better help-seeking skills by tracing students actions on a (meta)cognitive help-seeking model and giving students appropriate feedback.
In a series of in vivo experiments, the Help Tutor accurately detected help-seeking errors that were associated with poorer learning and with poorer declarative and procedural knowledge components of help seeking. The main findings were that students made fewer help-seeking errors while working with the Help Tutor and acquired better help seeking declarative knowledge components. However, we did not find evidence that this led to an improvement in learning at the domain level or to better help-seeking behavior in a paper-and-pencil environment. We pose a number of hypotheses in an attempt to explain these results. We question the current focus of metacognitive tutoring, and suggest ways to reexamine the role of help facilities and of metacognitive tutoring within Intelligent Tutoring Systems.
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Gaming the System
Two threads of work are included under this title: 'Gaming the system' is defined as making progress within the curriculum without thinking through the material. Most commonly, within Cognitive Tutors, this is done by guessing repeatedly, or asking fur quick successive hint requests until the answer is given by the system.For more details, see Ryan Baker's description.
Learning Goals within Intelligent Tutoring Systems - In this project we design an ACT-R model which tries to identify students' learning goals when interacting with Cognitive Tutors. This information can be used to adapt the instruction to students' learning goals.
Operator Application Reification
In this line of research we investigate what the sources of difficulty for compositve problems are. Even though problems involving several skills are harder than those involving only one skill at a time, our initial results suggest that it is the lack of sufficient proficiency on the single-skill problems that makes the multiple-skills problems more challenging.[top]