ethanporcaro/eric-fulltext-meta huggingface dataset:
Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning
Author: Bouchet, Francois
Date: 2012-06-00
Type: Reports - Research
Subjects: Tutoring,Feedback (Response),Intelligent Tutoring Systems,Academic Achievement,Student Characteristics,Evaluation,College Students,Higher Education,Pretests Posttests,Scores,Multivariate Analysis,Data

Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students learning about a complex science topic with an agent-based ITS that fosters self-regulated learning (SRL). Preliminary analysis with an Expectation-Maximization clustering algorithm revealed the existence of three distinct groups of students, distinguished by their test and quiz scores (low for the first group, medium for the second group, and high for the third group), their learning gains (low, medium, high), the frequency of their note-taking (rare, frequent, rare) and note-checking (rare, rare, frequent), the proportion of sub-goals attempted (low, low, high), and the time spent reading (high, high, low). In this paper, we extend this analysis to identify characteristic learning behaviors and strategies that distinguish these three groups of students. We employ a differential sequence mining technique to identify differentially frequent activity patterns between the student groups and interpret these patterns in terms of relevant learning behaviors. The results of this analysis reveal that high-performing students tend to be better at quickly identifying the relevance of a page to their subgoal, are more methodical in their exploration of the pedagogical content, rely on system prompts to take notes and summarize, and are more strategic in their preparation for the post-test (e.g., using the end of their session to briefly review pages). These results provide a first step in identifying the group to which a student belongs during the learning session, thus making possible a real-time adaptation of the system. (Contains 1 figure, 3 tables, and 1 footnote.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Assisting Instructional Assessment of Undergraduate Collaborative Wiki and SVN Activities
Author: Kim, Jihie
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Undergraduate Students,Engineering Education,Teamwork,Group Activities,Student Projects,Web 2.0 Technologies,Integrated Learning Systems,Computer Software,Student Participation,Data Analysis

In this paper we examine the collaborative performance of undergraduate engineering students who used shared project documents (Wikis, Google documents) and a software version control system (SVN) to support project collaboration. We present an initial implementation of TeamAnalytics, an instructional tool that facilitates the analyses of the student collaboration process by creating dynamic summaries of team member contributions over time in. Document content is processed using machine learning techniques. We validated the summary's effectiveness using a questionnaire given to instructors and team managers. Team managers indicated that summaries of student contribution to coding activities influenced their evaluation and coordination of team projects. (Contains 7 figures and 5 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.]
IRIS, Gender, and Student Achievement at University of Genova
Author: Bonfa, Antonella
Date: 2012-00-00
Type: Journal Articles
Subjects: Academic Achievement,Foreign Countries,Data Analysis,Gender Differences,Educational Indicators,Achievement Need,Guidance Programs,Prior Learning,Incidence,Academic Records,Student Records,Correlation,Performance Based Assessment,Performance Factors,Predictor Variables

The article analyses the gender effects on student achievement at University of Genova and it is a part of the research performed by the University of Genova called &quot;Benchmarks interfaculty students: Development of a gender perspective to find strategies to understand what leads students to success in their studies&quot;, financed by the Regional Office for Equality Gender and included in the IRIS (Indicatore di Rendimento Interfacolta Studenti (Interfaculty Students' Performance Indicator)) study, the survey implemented by the University of Genova since the AY (academic year) 2004/2005 to monitor student achievement at university. The aim of the article is to investigate students' achievements in higher education from a gender perspective through the interfaculty indicator of student achievement of the University of Genova, called IRIS. A secondary data analysis was undertaken with a quantitative approach to study the success of first-year students of the University of Genova in the AY 2009/2010. The students' sample was census, and it was clustered into the five university schools as provided by the new academic reform. Descriptive analysis, correlations, and a regression model were undertaken to focus on gender differences. The analysis was useful to show that female students are more successful than their male fellows and to find out some guidance policies for improving students' achievements according to gender differences. (Contains 6 tables and 2 figures.) [This research was financed by the Regional Office for Gender Equality of Liguria Region (Italy).]
Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction
Author: Trivedi, Shubhendu
Date: 2012-06-00
Type: Reports - Descriptive
Subjects: Homogeneous Grouping,Prediction,Tutors,Cluster Grouping,Pretests Posttests,Scores,Models,Comparative Analysis,Data,Data Analysis

Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of the members of the given cluster than a predictor trained on the entire data-set. Previous work has used this basic premise to construct a simple yet strong bagging strategy. However, such models have one significant drawback: Instances (such as students) are clustered while features (tutor usage features/items) are left alone. One-way clustering by using some objective function measures the degree of homogeneity between data instances. Often it is noticed that features also influence final prediction in homogeneous groups. This indicates a duality in the relationship between clusters of instances and clusters of features. Co-Clustering simultaneously measures the degree of homogeneity in both data instances and features, thus also achieving clustering and dimensionality reduction simultaneously. Students and features could be modelled as a bipartite graph and a simultaneous clustering could be posed as a bipartite graph partitioning problem. In this paper we integrate an effective bagging strategy with Co-Clustering and present results for prediction of out-of-tutor performance of students. We report that such a strategy is very useful and intuitive, even improving upon performance achieved by previous work. (Contains 4 figures and 2 tables.) [Additional funding for this research was provided by the United States Army. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution
Author: Kinnebrew, John S.
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Teaching Methods,Mathematics,Behavior Patterns,Academic Achievement,Data,Feedback (Response),Metacognition,Comparative Analysis

Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to identify differentially frequent patterns between two predefined groups. We extend this technique by contextualizing the sequence mining with information on the student's task performance and learning activities. Specifically, we study transformation of action sequences using action features, such as activity categorizations, relevance and timing between actions, and repetition of analogous actions. We employ a piecewise linear segmentation algorithm in concert with the action transformation and differential sequence mining techniques to identify and compare segments of students' productive and unproductive learning behaviors. We present the results of this methodology applied to a recent middle school class study, in which students learned about climate change. Our primary focus in this analysis is the effectiveness and variation in the reading behaviors of high- versus low-performing students. These results illustrate the potential of this iterative methodology in identifying and interpreting learning behavior patterns at multiple levels of detail. (Contains 5 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 Impact on Individualizing Student Models on Necessary Practice Opportunities
Author: Lee, Jung In
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Intelligent Tutoring Systems,Tutors,Tutoring,Regression (Statistics),Academic Achievement,Data,Prediction,Probability

When modeling student learning, tutors that use the Knowledge Tracing framework often assume that all students have the same set of model parameters. We find that when fitting parameters to individual students, there is significant variation among the individual's parameters. We examine if this variation is important in terms of instructional decisions by computing the difference in the expected number of practice opportunities required if mastery is assessed using an individual student's own estimated model parameters, compared to the population model. In the dataset considered, we find that a significant portion of students are expected to perform twice as many practice opportunities if the student is modeled using a population-based model, compared to the number needed if the student's own model parameters were used. We also find an additional significant portion of students will be likely to receive less practice opportunities than needed, implying that such students will be advanced too early. Though further work on additional datasets is needed to explore this issue in more depth, our results suggest that considering individual variation in student parameters may have important implications for the instructional decisions made in intelligent tutoring systems that use a Knowledge Tracing model. (Contains 3 figures and 1 footnote.) [This paper was supported by Google. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory
Author: Bergner, Yoav
Date: 2012-06-00
Type: Reports - Evaluative
Subjects: Factor Analysis,Prediction,Item Response Theory,Student Reaction,Man Machine Systems,Multivariate Analysis,Probability,Comparative Analysis,Algebra,Geometric Concepts,Simulation

We apply collaborative filtering (CF) to dichotomously scored student response data (right, wrong, or no interaction), finding optimal parameters for each student and item based on cross-validated prediction accuracy. The approach is naturally suited to comparing different models, both unidimensional and multidimensional in ability, including a widely used subset of Item Response Theory (IRT) models which obtain as specific instances of the CF: the one-parameter logistic (Rasch) model, Birnbaum's 2PL model, and Reckase's multidimensional generalization M2PL. We find that IRT models perform well relative to generalized alternatives, and thus this method offers a fast and stable alternate approach to IRT parameter estimation. Using both real and simulated data we examine cases where one- or two-dimensional IRT models prevail and are not improved by increasing the number of features. Model selection is based on prediction accuracy of the CF, though it is shown to be consistent with factor analysis. In multidimensional cases the item parameterizations can be used in conjunction with cluster analysis to identify groups of items which measure different ability dimensions. (Contains 7 figures.) [This paper was also supported by the Massachusetts Institute of Technology. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
Working Together to Achieve Greater Impact: The Donors' Education Collaborative of New York City. Principles for Effective Education Grantmaking. Case in Brief Number 3
Author: Grantmakers for Education
Date: 2012-00-00
Type: Reports - Descriptive
Subjects: Program Effectiveness,Program Implementation,Donors,Public Education,Expertise,Governance,Educational Change,Public Support,Private Financial Support,Grants,Parents,Parent Participation,Immigrants,Educational Improvement,Philanthropic Foundations

Since the mid-1990s, constituency building and advocacy for better public education have grown steadily in New York City. &quot;Working Together to Achieve Greater Impact&quot; explores how that growth was fueled by the Donors' Education Collaborative of New York, which pools its members' financial resources and expertise to advance shared grantmaking goals. The case study illustrates the importance--and challenges--of the sixth of Grantmakers for Education's eight Principles for Effective Education Grantmaking: &quot;leverage,&quot; &quot;influence&quot; and &quot;collaboration.&quot; [For the full report, &quot;Working Together to Achieve Greater Impact: The Donors' Education Collaborative of New York City. Principles for Effective Education Grantmaking. Case Study Number 3,&quot; see ED537197.]
Persistence, Partnership and Public Will: The Annie E. Casey Foundation's Investments in Kentucky School Reform. Principles for Effective Education Grantmaking. Case in Brief Number 2
Author: Grantmakers for Education
Date: 2012-00-00
Type: Reports - Descriptive
Subjects: Persistence,Educational Change,State Legislation,Disadvantaged Youth,Private Financial Support,Grants,Philanthropic Foundations,Educational Improvement

&quot;Persistence, Partnership and Public Will&quot; explores the sustained role the Annie E. Casey Foundation played in Kentucky for more than a decade to help create an environment in which the state's ambitious and comprehensive effort to improve education for all of its students would have the time, resources and attention needed to prove its worth. The case study illustrates the importance--and challenges--of two of Grantmakers for Education's eight Principles for Effective Education Grantmaking: &quot;persistence&quot; and &quot;engaged partners.&quot; [For the full report, &quot;Persistence, Partnership and Public Will: The Annie E. Casey Foundation's Investments in Kentucky School Reform. Principles for Effective Education Grantmaking. Case Study Number 2,&quot; see ED537199.]
Linking Resources to Results: The Chicago Public Education Fund's Master Teacher Initiative. Principles for Effective Education Grantmaking. Case in Brief Number 1
Author: Grantmakers for Education
Date: 2012-00-00
Type: Reports - Descriptive
Subjects: Urban Schools,Public Education,School Administration,Teacher Effectiveness,Teacher Recruitment,Master Teachers,Sustainability,Government School Relationship,Financial Support,Educational Objectives,Accountability,Compensation (Remuneration),Disadvantaged Schools,Teacher Certification,National Programs,Incentives,Resource Allocation,Strategic Planning

&quot;Linking Resources to Results&quot; explores the deliberate approach--including specific goals, a clearly articulated grantmaking strategy and benchmarks for measuring progress--the Chicago Public Education Fund used to help the Chicago Public Schools recognize, reward and deploy high-quality teachers. The case study is intended to help grantmakers see ways to more deliberately &quot;link resources to desired results,&quot; which is one of Grantmakers for Education's Principles for Effective Education Grantmaking. [For the full report, &quot;Linking Resources to Results: The Chicago Public Education Fund's Master Teacher Initiative. Principles for Effective Education Grantmaking. Case Study Number 1,&quot; see ED537179.]
Persistence, Partnership and Public Will: The Annie E. Casey Foundation's Investments in Kentucky School Reform. Principles for Effective Education Grantmaking. Case Study Number 2
Author: Sherwood, Kay E.
Date: 2006-04-00
Type: Reports - Descriptive
Subjects: Persistence,Children,Public Education,Disadvantaged Youth,Private Financial Support,Grants,Philanthropic Foundations,Educational Improvement

This case study explores the sustained role the Annie E. Casey Foundation played for more than a decade to help create an environment in which an ambitious and comprehensive effort to improve public education for all children in Kentucky would have the time, resources and attention needed to prove its worth. As the case describes, the sweeping goals and aspirations of Kentucky's school reform efforts--revolutionary in 1990--caught the attention of the Annie E. Casey Foundation. The foundation's mission is to build better futures for disadvantaged children who are at risk of poor educational, economic, social and health outcomes, and it believes that changing public systems is essential to making progress in issues regarding public welfare. Interested in expanding its grantmaking in education, the foundation concluded it could learn a great deal from working in Kentucky. (Contains 19 footnotes.) [For &quot;Persistence, Partnership and Public Will: The Annie E. Casey Foundation's Investments in Kentucky School Reform. Principles for Effective Education Grantmaking. Case in Brief Number 2,&quot; see ED537196.]
Focus for Impact: The PacifiCorp Foundation for Learning's Early Childhood Literacy Initiative. Principles for Effective Education Grantmaking. Case Study Number 5
Author: King, Caroline
Date: 2006-09-00
Type: Reports - Descriptive
Subjects: Partnerships in Education,Program Effectiveness,Lifelong Learning,Emergent Literacy,Private Financial Support,Children,Educational Change,Grants,Persistence,Sustainability

The PacifiCorp Foundation for Learning was at a turning point in August 2006. It had been five years since the corporate foundation had shifted its focus from general-purpose grantmaking to supporting individual and community learning, and its flagship Early Childhood Literacy Initiative--launched in 2003 to raise literacy rates in the communities served by PacifiCorp power companies--was nearing the end of its grant term. The multiyear $1.3 million initiative had supported five grantees--one from each of the western states where PacifiCorp concentrates its business--representing a range of approaches to improving early literacy. The initiative was considered unique for a small corporate philanthropy, particularly within the utility industry, given the foundation's discrete area of focus, proactive selection of programs and ongoing support for grantees. Also noteworthy were the results of the four-year initiative: emerging evaluation data suggested its programs were contributing to increased early childhood literacy rates and all five grantees were on the road to becoming self-sustaining. To better advance its mission of promoting lifelong learning, the PacifiCorp Foundation committed in 2001 to devote 20 percent of its grantmaking budget to a long-term initiative. Established by PacifiCorp, a regional power company, the PacifiCorp Foundation's annual grantmaking budget is $2.5-3 million. It awards grants to nonprofits in the western states in which the company does business. This case study is intended to help funders see ways of applying &quot;discipline and focus&quot;--one of Grantmakers for Education's Principles for Effective Education Grantmaking--to their work. Because public dollars in education will always dwarf private investments, a funder has greater impact when grantmaking aims  to impact a specific social need, is carefully planned and targeted, and concentrates resources to make a difference. PacifiCorp's culture emphasizes solving problems by going &quot;upstream&quot; to intervene at the earliest possible opportunity, and so the foundation trustees chose the area of early education literacy for the first initiative. At that time, the foundation also chose nonprofit partners in Oregon, Utah and Wyoming (and, later, Idaho and Washington) to implement or scale up a highly effective childhood literacy program. (Contains 3 tables, 3 exhibits and 9 footnotes.) [Additional funding for this paper was provided by Washington Mutual. For &quot;Focus for Impact: The PacifiCorp Foundation for Learning's Early Childhood Literacy Initiative. Principles for Effective Education Grantmaking. Case in Brief Number 5,&quot; see ED537178.]
Automated Student Model Improvement
Author: Koedinger, Kenneth R.
Date: 2012-06-00
Type: Reports - Descriptive
Subjects: Educational Technology,Intelligent Tutoring Systems,Educational Improvement,Mathematics,Academic Achievement,Tutors,Models,Evaluation

Student modeling plays a critical role in developing and improving instruction and instructional technologies. We present a technique for automated improvement of student models that leverages the DataShop repository, crowd sourcing, and a version of the Learning Factors Analysis algorithm. We demonstrate this method on eleven educational technology data sets from intelligent tutors to games in a variety of domains from math to second language learning. In at least ten of the eleven cases, the method discovers improved models based on better test-set prediction in cross validation. The improvements isolate flaws in the original student models, and we show how focused investigation of flawed parts of models leads to new insights into the student learning process and suggests specific improvements for tutor design. We also discuss the great potential for future work that substitutes alternative statistical models of learning from the EDM literature or alternative model search algorithms. (Contains 6 figures and 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.]