ethanporcaro/eric-fulltext-meta huggingface dataset:
Mining Concept Maps to Understand University Students' Learning
Author: Yoo, Jin Soung
Date: 2012-06-00
Type: Reports - Research
Subjects: Concept Mapping,College Students,Research Tools,Higher Education,Academic Achievement,Data,Maps

Concept maps, visual representations of knowledge, are used in an educational context as a way to represent students' knowledge, and identify mental models of students; however there is a limitation of using concept mapping due to its difficulty to evaluate the concept maps. A concept map has a complex structure which is composed of concepts and their relationships that often have a weighted direction. This work explores the feasibility of the analysis of concept maps using data mining methods, and investigate the possibility of using concept maps as a research tool to understand college student's learning. A total of 111 college students participated in this study. The findings from frequent concept mining and sub-concept map mining suggest that students expect a traditional way of learning. The study also shows a promising area of further study in the area of data mining in education. (Contains 2 figures and 1 table.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Meta-Learning Approach for Automatic Parameter Tuning: A Case Study with Educational Datasets
Author: Molina, M. M.
Date: 2012-06-00
Type: Reports - Research
Subjects: Case Studies,Mathematics,Data Analysis,Accuracy,Classification,Prediction,Undergraduate Students,Secondary School Students,Foreign Countries

This paper proposes to the use of a meta-learning approach for automatic parameter tuning of a well-known decision tree algorithm by using past information about algorithm executions. Fourteen educational datasets were analysed using various combinations of parameter values to examine the effects of the parameter values on accuracy classification. Then, the new meta-dataset was used to predict the classification accuracy on the basis of the value parameters and some characteristics of the dataset. The obtained classification models can help us decide how the default parameters should be tuned in order to increase the accuracy of the classifier when using different types of educational datasets. (Contains 3 figures and 3 tables.) [For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012),&quot; see ED537074.]
Interleaved Practice with Multiple Representations: Analyses with Knowledge Tracing Based Techniques
Author: Rau, Martina A.
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Intelligent Tutoring Systems,Mathematics,Knowledge Level,Scheduling,Visual Aids,Pretests Posttests,Data Analysis,Bayesian Statistics,Network Analysis,Models,Learning,Elementary School Students,Grade 4,Grade 5

The goal of this paper is to use Knowledge Tracing to augment the results obtained from an experiment that investigated the effects of practice schedules using an intelligent tutoring system for fractions. Specifically, this experiment compared different practice schedules of multiple representations of fractions: representations were presented to students either in an interleaved or in a blocked fashion. The results obtained from posttests demonstrate an advantage of interleaving representations. Using methods derived from Knowledge Tracing, we investigate whether we can replicate the contextual interference effect, an effect commonly found when investigating practice schedules of different task types. Different Knowledge Tracing models were adapted and compared. A model that included practice schedules as a predictor of students' learning was most successful. A comparison of learning rate estimates between conditions shows that even during the acquisition phase, students working with interleaved representations demonstrate higher learning rates. This finding stands in contrast to the commonly found contextual interference effect when interleaving task types. We reflect on the practical and theoretical implications of these findings. (Contains 1 figure and 3 tables.) [For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012),&quot; see ED537074.]
Calculating Probabilistic Distance to Solution in a Complex Problem Solving Domain
Author: Sudol, Leigh Ann
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Problem Solving,Tutors,Feedback (Response),Probability,Intelligent Tutoring Systems,Academic Achievement,Markov Processes,Programming

In complex problem solving domains, correct solutions are often comprised of a combination of individual components. Students usually go through several attempts, each attempt reflecting an individual solution state that can be observed during practice. Classic metrics to measure student performance over time rely on counting the number of submissions or focusing on time taken to complete the problem correctly. These metrics are not robust to the correction of errors that may increase problem solving time, and do not reflect topical misunderstandings on the part of the student. In this paper we propose a metric to measure the probabilistic distance between an observed student solution and a correct solution. Students working in an online programming environment completed four practice problems. Their submissions were then evaluated against a model of the algorithmic components necessary for a correct solution. A Markov Model was used to generate a problem state graph. Our proposed Probabilistic Distance to Solution (PDS) metric was applied to the graph to determine the distance, in program states, from an observed program model to the model of a correct solution. Results indicate that the PDS is useful in determining if an edit or student path is (a) typical of students who have mastered content, and (b) productive in progressing toward a solution. We offer implementation details of PDS and implications for future work based upon current observations. (Contains 2 figures, 1 table, and 3 footnotes.)[For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Identifying Successful Learners from Interaction Behaviour
Author: McCuaig, Judi
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Interaction,Integrated Learning Systems,Data,Academic Achievement,Self Evaluation (Individuals),Correlation,Prediction,Grades (Scholastic),Behavior

The interaction behaviours of successful, high-achieving learners when using a Learning Management System (LMS) are different than the behaviours of learners who are having more difficulty mastering the course material. This paper explores the idea that conventional Learning Management Systems can exploit data mining techniques to predict the success or failure of students without requiring the results of formal assessments. This paper describes a study with a second semester computer science class that shows that the success or failure of a learner can be predicted using information about learner interactions with course materials and learner self-reports of subject matter confidence. (Contains 6 figures and 3 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Classification via Clustering for Predicting Final Marks Based on Student Participation in Forums
Author: Lopez, M. I.
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Classification,Prediction,Grades (Scholastic),College Freshmen,Student Participation,Discussion Groups,Predictor Variables,Accuracy,Mathematics,Social Networks,Network Analysis

This paper proposes a classification via clustering approach to predict the final marks in a university course on the basis of forum data. The objective is twofold: to determine if student participation in the course forum can be a good predictor of the final marks for the course and to examine whether the proposed classification via clustering approach can obtain similar accuracy to traditional classification algorithms. Experiments were carried out using real data from first-year university students. Several clustering algorithms using the proposed approach were compared with traditional classification algorithms in predicting whether students pass or fail the course on the basis of their Moodle forum usage data. The results show that the Expectation-Maximisation (EM) clustering algorithm yields results similar to those of the best classification algorithms, especially when using only a group of selected attributes. Finally, the centroids of the EM clusters are described to show the relationship between the two clusters and the two classes of students. (Contains 2 figures and 6 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Development of a Workbench to Address the Educational Data Mining Bottleneck
Author: Rodrigo, Ma. Mercedes T.
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Thinking Skills,Educational Technology,Educational Research,Computer Software,Mathematics,Data,Academic Achievement,Intelligent Tutoring Systems,Tutors,Tutoring,Cooperative Learning

In recent years, machine-learning software packages have made it easier for educational data mining researchers to create real-time detectors of cognitive skill as well as of metacognitive and motivational behavior that can be used to improve student learning. However, there remain challenges to overcome for these methods to become available to the wider educational research and practice communities, including developing the labels that support supervised learning, distilling relevant and appropriate data features, and setting up appropriate cross-validation and configuration algorithms. We discuss the development of an Educational Data Mining (EDM) Workbench designed to address these challenges. (Contains 3 figures.) [This research project was made possible through a grant from the Philippines Department of Science and Technology's Engineering Research and Technology for Development program entitled "Development of an Educational Data Mining Workbench." For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Interaction Networks: Generating High Level Hints Based on Network Community Clustering
Author: Eagle, Michael
Date: 2012-06-00
Type: Reports - Descriptive
Subjects: Data Analysis,Interaction,Network Analysis,Problem Solving,Feedback (Response),Intelligent Tutoring Systems,Graphs

We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals. The preliminary results show that using network analysis techniques are promising for exploring and understanding user data from open problem solving environments. (Contains 3 figures.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Using Edit Distance to Analyse Errors in a Natural Language to Logic Translation Corpus
Author: Barker-Plummer, Dave
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Logical Thinking,Grading,Natural Language Processing,Translation,Sentences,Mathematics,Homogeneous Grouping,Students

We have assembled a large corpus of student submissions to an automatic grading system, where the subject matter involves the translation of natural language sentences into propositional logic. Of the 2.3 million translation instances in the corpus, 286,000 (approximately 12%) are categorized as being in error. We want to understand the nature of the errors that students make, so that we can develop tools and supporting infrastructure that help students with the problems that these errors represent. With this aim in mind, this paper describes an analysis of a significant proportion of the data, using edit distance between incorrect answers and their corresponding correct solutions, and the associated edit sequences, as a means of organising the data and detecting categories of errors. We demonstrate that a large proportion of errors can be accounted for by means of a small number of relatively simple error types, and that the method draws attention to interesting phenomena in the data set. (Contains 7 figures, 3 tables, and 9 footnotes.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Early Prediction of Student Self-Regulation Strategies by Combining Multiple Models
Author: Sabourin, Jennifer L.
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Computer Assisted Instruction,Goal Orientation,Prediction,Classification,Regression (Statistics),Data,Man Machine Systems,Scaffolding (Teaching Technique),Teaching Methods

Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has been the subject of increasing interest. Unfortunately, monitoring these behaviors in real-time has proven challenging. This paper explores a variety of data mining approaches to predicting student self-regulation capabilities. Students are classified into SRL-use categories based on evidence of goal-setting and monitoring activities. Prior work on early prediction of these categories pointed to logistic regression and decision tree models as effective techniques. This paper builds on these findings by exploring techniques by which these models can be combined to improve classification accuracy and early prediction capabilities. By improving classification accuracy, this work can be leveraged in the design of computer-based learning environments to provide adaptive scaffolding of self-regulation behaviors. (Contains 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Policy Building--An Extension to User Modeling
Author: Yudelson, Michael V.
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Scores,Tests,Regression (Statistics),Pretests Posttests,Data,Factor Analysis,Algebra,Mathematics Instruction,Grade 6,Grade 7,Middle School Students,Tutoring,Academic Achievement

In this paper we combine a logistic regression student model with an exercise selection procedure. As opposed to the body of prior work on strategies for selecting practice opportunities, we are working on an assumption of a finite amount of opportunities to teach the student. Our goal is to prescribe activities that would maximize the amount learned as evaluated by expected post-test success. We evaluate the proposed approach using an existing dataset where data was collected performing random skill selection. Our results cautiously support the hypothesis that using policies designed to optimize the post-test score associated with higher learning outcomes, but more work is needed. (Contains 2 figures, 3 tables, and 1 footnote.) [For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012),&quot; see ED537074.]
Learning Gains for Core Concepts in a Serious Game on Scientific Reasoning
Author: Forsyth, Carol
Date: 2012-06-00
Type: Reports - Research
Subjects: Learning,Cognitive Processes,Logical Thinking,Scientific Methodology,Inquiry,Fundamental Concepts,Intelligent Tutoring Systems,Educational Games,Pretests Posttests,College Students,Regression (Statistics),Validity

&quot;OperationARIES!&quot; is an Intelligent Tutoring System that teaches scientific inquiry skills in a game-like atmosphere. Students complete three different training modules, each with natural language conversations, in order to acquire deep-level knowledge of 21 core concepts of research methodology (e.g., correlation does not mean causation). The student first acquires basic declarative knowledge and then applies the knowledge by critiquing case studies on scientific methodology and finally generating questions that reflect the core topics. A study using a pretest-training-posttest design was conducted in which 46 college students interacted with the modules of &quot;OperationARIES!&quot;, resulting in thousands of logged measures. The goal of this investigation was to discover the different trajectories of learning within 11 of these core concepts by evaluating 3 main constructs (e.g., discrimination, generation, and time on task) represented by key logged measures. Different constructs showed relationships with specific core concepts. Three core concepts were analyzed with stepwise regression and 5-fold cross-validation in order to discover contributing factors to learning gains for these core concepts. [For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012),&quot; see ED537074.]
Leveraging First Response Time into the Knowledge Tracing Model
Author: Wang, Yutao
Date: 2012-06-00
Type: Reports - Research
Subjects: Reaction Time,Students,Knowledge Level,Models,Intelligent Tutoring Systems,Prediction,Accuracy,Elementary Secondary Education

The field of educational data mining has been using the Knowledge Tracing model, which only look at the correctness of student first response, for tracking student knowledge. Recently, lots of other features are studied to extend the Knowledge Tracing model to better model student knowledge. The goal of this paper is to analyze whether or not the information of student first response time of a question can be leveraged into Knowledge Tracing model and improve Knowledge Tracing's prediction accuracy. In our experiments, we used discretized first response time data to predict students' correctness of the next question, and leveraged the result into a Knowledge Tracing model. Our analysis confirmed the value of student first response time in modeling student knowledge. (Contains 2 figures and 2 tables.) [Additional funding for this research was provided by Neil Heffernan's CAREER grant. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
The Real World Significance of Performance Prediction
Author: Pardos, Zachary A.
Date: 2012-06-00
Type: Reports - Research
Subjects: High Stakes Tests,Prediction,Standardized Tests,Simulation,Tutors,Scores,Data,Evaluation,Inferences,Academic Achievement,Mathematics

In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world significance of the differences in errors has been largely unexplored. In this paper we take a deeper look at what reported errors actually mean in the context of high stakes test score prediction as well as student mastery prediction. We report how differences in common error and accuracy metrics on prediction tasks translate to impact on students and depict how standard validation methods can lead to overestimated accuracies in these prediction tasks. Two years of student tutor use and corresponding student state test scores are used for the analysis of test prediction while a simulation study is conducted to investigate the correspondence between performance prediction error and latent knowledge prediction. (Contains 7 tables and 2 footnotes.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
The Rise of the Super Experiment
Author: Stamper, John C.
Date: 2012-06-00
Type: Reports - Research
Subjects: Internet,Feedback (Response),Computer Software,Data Collection,Computer Uses in Education,Laboratory Experiments,Mathematics Instruction,Educational Games,Research Methodology,Numeracy,Mathematics Skills,Elementary School Mathematics,Educational Technology

Traditional experimental paradigms have focused on executing experiments in a lab setting and eventually moving successful findings to larger experiments in the field. However, data from field experiments can also be used to inform new lab experiments. Now, with the advent of large student populations using internet-based learning software, online experiments can serve as a third setting for experimental data collection. In this paper, we introduce the Super Experiment Framework (SEF), which describes how internet-scale experiments can inform and be informed by classroom and lab experiments. We apply the framework to a research project implementing learning games for mathematics that is collecting hundreds of thousands of data trials weekly. We show that the framework allows findings from the lab-scale, classroom-scale and internet-scale experiments to inform each other in a rapid complementary feedback loop. (Contains 2 figures and 2 tables.) [This research was supported by Next Generation Learning Challenge, Carlow University, and Pellisippi State University. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
New York University: Documentation of the Teachers for a New Era Learning Network. Case Study
Author: Academy for Educational Development
Date: 2012-00-00
Type: Reports - Descriptive
Subjects: Teacher Education Programs,Preservice Teacher Education,Student Teachers,Student Teacher Evaluation,Schools of Education,Evidence,Organizational Change,Networks,Case Studies,Interviews,Observation,Rating Scales,Evaluation Methods,Outcome Measures,Program Effectiveness,Program Evaluation,Program Descriptions,Educational Change,Urban Education,Urban Universities,Partnerships in Education,College School Cooperation,Beginning Teachers,Sustainability

The Academy for Educational Development (AED) sent a research team to New York University (NYU) on December 8-9, 2008 to conduct interviews with individuals who play important roles in the university's teacher preparation program. These interviews, along with additional documentation provided by NYU and identified by the AED research team, provide the basis for this case study. This case study is one of nine prepared by AED to document evidence of institutional change in teacher preparation at nine of the 30 universities that took part in the Teachers for A New Era (TNE) Learning Network, an initiative supported beginning in 2005 by a grant from the Annenberg Foundation, with additional funding from the Carnegie Corporation of New York. AED selected the nine universities based upon a variety of factors, including their degree of engagement in the Learning Network, and their willingness to specify a program objective and indicator(s) of change that reflected important work underway and would serve as the focus of this case study. Appended are: (1) Interviewees; and (2) Learning Network--Measuring Progress. (Contains 4 footnotes.)