12,154 research outputs found
OFMTutor: An operator function model intelligent tutoring system
The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described
Refining Prerequisite Skill Structure Graphs Using Randomized Controlled Trials
Prerequisite skill structure graphs represent the relationships between knowledge components. Prerequisite structure graphs also propose the order in which students in a given curriculum need to be taught specific knowledge components in order to assist them build on previous knowledge and improve achievement in those subject domains. The importance of accurate prerequisite skill structure graphs can therefore not be overemphasized. In view of this, many approaches have been employed by domain experts to design and implement these prerequisite structures. A number of data mining techniques have also been proposed to infer these knowledge structures from learner performance data. These methods have achieved varied degrees of success. Moreover, to the best of our knowledge, none of the methods have employed extensive randomized controlled trials to learn about prerequisite skill relationships among skills. In this dissertation, we motivate the need for using randomized controlled trials to refine prerequisite skill structure graphs. Additionally, we present PLACEments, an adaptive testing system that uses a prerequisite skill structure graph to identify gaps in students’ knowledge. Students with identified gaps are assisted with more practice assignments to ensure that the gaps are closed. PLACEments additionally allows for randomized controlled experiments to be performed on the underlying prerequisite skill structure graph for the purpose of refining the structure. We present some of the different experiment categories which are possible in PLACEments and report the results of one of these experiment categories. The ultimate goal is to inform domain experts and curriculum designers as they create policies that govern the sequencing and pacing of contents in learning domains whose content lend themselves to sequencing. By extension students and teachers who apply these policies benefit from the findings of these experiments
Refining Learning Maps with Data Fitting Techniques
Learning maps have been used to represent student knowledge for many years. These maps are usually hand made by experts in a given domain. However, these hand-made maps have not been found to be predictive of student performance. Several methods have been proposed to find bet-ter fitting learning maps. These methods include the Learning Factors Analysis (LFA) model and the Rule-space method. In this thesis we report on the application of one of the proposed operations in the LFA method to a small section of a skill graph and develop a greedy search algorithm for finding better fitting models for this graph. Additionally an investigation of the factors that influence the search for better data fitting models using the proposed algorithm is reported. We also present an empirical study in which PLACEments, an adaptive testing system that employs a skill graph, is modified to test the strength of prerequisite skill links in a given learning map and propose a method for refining learning maps based on those findings. It was found that the proposed greedy search algorithm performs as well as an original skill graph but with a smaller set of skills in the graph. Additionally it was found that, among other factors, the number of unnecessary skills, the number of items in the graph, and the guess and slip rates of the items tagged with skills in the graph have an impact on the search. Further, the size of the evaluation data set impacts the search. The more data there is for the search, the more predictive the learned skill graph. Additionally, PLACEments, an adaptive testing feature of ASSISTments, has been found to be useful for refining skill graphs by detecting the strengths of prerequisite links between skills in a graph
Pervasive Parallel And Distributed Computing In A Liberal Arts College Curriculum
We present a model for incorporating parallel and distributed computing (PDC) throughout an undergraduate CS curriculum. Our curriculum is designed to introduce students early to parallel and distributed computing topics and to expose students to these topics repeatedly in the context of a wide variety of CS courses. The key to our approach is the development of a required intermediate-level course that serves as a introduction to computer systems and parallel computing. It serves as a requirement for every CS major and minor and is a prerequisite to upper-level courses that expand on parallel and distributed computing topics in different contexts. With the addition of this new course, we are able to easily make room in upper-level courses to add and expand parallel and distributed computing topics. The goal of our curricular design is to ensure that every graduating CS major has exposure to parallel and distributed computing, with both a breadth and depth of coverage. Our curriculum is particularly designed for the constraints of a small liberal arts college, however, much of its ideas and its design are applicable to any undergraduate CS curriculum
Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data
Understanding a student's problem-solving strategy can have a significant
impact on effective math learning using Intelligent Tutoring Systems (ITSs) and
Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better
personalize itself to correct specific misconceptions that are indicated by
incorrect strategies, specific problems can be designed to improve strategies
and frustration can be minimized by adapting to a student's natural way of
thinking rather than trying to fit a standard strategy for all. While it may be
possible for human experts to identify strategies manually in classroom
settings with sufficient student interaction, it is not possible to scale this
up to big data. Therefore, we leverage advances in Machine Learning and AI
methods to perform scalable strategy prediction that is also fair to students
at all skill levels. Specifically, we develop an embedding called MVec where we
learn a representation based on the mastery of students. We then cluster these
embeddings with a non-parametric clustering method where we progressively learn
clusters such that we group together instances that have approximately
symmetrical strategies. The strategy prediction model is trained on instances
sampled from these clusters. This ensures that we train the model over diverse
strategies and also that strategies from a particular group do not bias the DNN
model, thus allowing it to optimize its parameters over all groups. Using real
world large-scale student interaction datasets from MATHia, we implement our
approach using transformers and Node2Vec for learning the mastery embeddings
and LSTMs for predicting strategies. We show that our approach can scale up to
achieve high accuracy by training on a small sample of a large dataset and also
has predictive equality, i.e., it can predict strategies equally well for
learners at diverse skill levels.Comment: 12 pages, 7 figures Published as a full paper in the 16th
International Conference on Educational Data Mining 202
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