ethanporcaro/eric-fulltext-meta huggingface dataset:
Recommendation in Higher Education Using Data Mining Techniques
Author: Vialardi, Cesar
Date: 2009-07-00
Type: Reports - Evaluative
Subjects: Data Analysis,Higher Education,Course Selection (Students),Enrollment,College Students,Engineering Education,Data Processing,Foreign Countries

One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to take decisions on their academic itineraries. More specifically, it provides support for the student to better choose how many and which courses to enrol on, having as  basis the experience of previous students with similar academic achievements. For this purpose, we have analyzed real data corresponding to seven years of student enrolment at the School of system Engineering at Universidad de Lima. Based on this analysis, a recommendation system was developed. (Contains 1 figure and 1 table.) [Funding was provided by Spanish Ministry of Science and Education. For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009),&quot; see ED539041.]
Automatic Detection of Student Mental Models during Prior Knowledge Activation in MetaTutor
Author: Rus, Vasile
Date: 2009-07-00
Type: Reports - Descriptive
Subjects: Data Analysis,Prior Learning,Cognitive Structures,College Students,High School Seniors,Intelligent Tutoring Systems,Self Control,Science Instruction,Essays,Artificial Intelligence,Automation,Mathematics,Comparative Analysis

This paper presents several methods to automatically detecting students' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students' mental models based on student-generated paragraphs during prior knowledge activation, a self-regulatory process. We describe two major categories of methods and combine each method with various machine learning algorithms. A detailed comparison among the methods and across all algorithms is also provided. The evaluation of the proposed methods is performed by comparing the prediction of the methods with human judgments on a set of 309 prior knowledge activation paragraphs collected from previous experiments with MetaTutor on college students. According to our experiments, a content-based method with word-weighting and Bayes Nets algorithm is the most accurate. (Contains 1 figure and 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]
Collaborative Data Mining Tool for Education
Author: Garcia, Enrique
Date: 2009-07-00
Type: Reports - Descriptive
Subjects: Data Analysis,Electronic Learning,Teacher Collaboration,Instructional Improvement,Client Server Architecture,Foreign Countries

This paper describes a collaborative educational data mining tool based on association rule mining for the continuous improvement of e-learning courses allowing teachers with similar course's profile sharing and scoring the discovered information. This mining tool is oriented to be used by instructors non experts in data mining such that, its internal operation is transparent to the user and the instructor can be focused in to the analysis of the results and make decisions about how to improve e-learning courses. (Contains 5 figures.) [Funding was provided by the Spanish Department of Research. For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009),&quot; see ED539041.]
Obtaining Rubric Weights for Assessments by More than One Lecturer Using a Pairwise Learning Model
Author: Quevedo, J. R.
Date: 2009-07-00
Type: Reports - Descriptive
Subjects: Data Analysis,Scoring Rubrics,Evaluation Criteria,Automation,Weighted Scores,Artificial Intelligence,Computer Science Education,College Students,College Faculty,Preferences

Specifying the criteria of a rubric to assess an activity, establishing the different quality levels of proficiency of development and defining weights for every criterion is not as easy as one a priori might think. Besides, the complexity of these tasks increases when they involve more than one lecturer. Reaching an agreement about the criteria and the levels of proficiency might be easier taking into account the abilities students must achieve according to the purpose of the subject. However, the disagreement about the weights of every criterion in an assessment rubric might easily appear. This paper focuses on the automatic weight adjustment for the criteria of a rubric. This fitting can be considered as a global perception that the whole group of lecturers have about the accuracy of solving an activity. Firstly, each lecturer makes a proposal of weights and then, from a set of pairs of students he/she globally expresses who of each pair has solved better the activity for which the rubric was designed. Secondly, an approach based on the pairwise learning is proposed in this work to obtain adequate weights for the criteria of a rubric. The system commits fewer errors than the lecturers and make them improve and reconsider some aspects of the rubric. (Contains 3 figures and 5 tables.) [Funding was provided by the Ministerio de Ciencia e Innovacion (MICINN). For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]
Visualization of Differences in Data Measuring Mathematical Skills
Author: Zoubek, Lukas
Date: 2009-07-00
Type: Reports - Descriptive
Subjects: Secondary School Students,Foreign Countries,Data Analysis,Visualization,Mathematics Skills,Measurement Techniques,Hypothesis Testing,Statistical Analysis,Secondary School Mathematics,Mathematical Concepts

Identification of significant differences in sets of data is a common task of data mining. This paper describes a novel visualization technique that allows the user to interactively explore and analyze differences in mean values of analyzed attributes. Statistical tests of hypotheses are used to identify the significant differences and the results are then presented using Hasse diagrams. The presented technique has been tested on real data coming from pedagogical tests focused on evaluation of mathematical skills of secondary school students in Czech Republic. The results show that the proposed tool provides comprehensible representation of data. (Contains 4 figures and 4 tables.) [For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009),&quot; see ED539041.]
Back to the Future: A Non-Automated Method of Constructing Transfer Models
Author: Feng, Mingyu
Date: 2009-07-00
Type: Reports - Evaluative
Subjects: Data Analysis,Models,Transfer of Training,Intelligent Tutoring Systems,Mathematics Instruction,Middle School Students,Grade 8,Goodness of Fit,School Effectiveness,Individual Differences,Student Characteristics

Representing domain knowledge is important for constructing educational software, and automated approaches have been proposed to construct and refine such models. In this paper, instead of applying automated and computationally intensive approaches, we simply start with existing hand-constructed transfer models at various levels of granularity and use them as a lens to examine student learning. Specifically, we are interested in seeing whether we can evaluate schools by examining the grain-size at which its students are best represented. Also, we are curious about whether different types of students are best represented by different transfer models. We found that better schools and stronger students are best represented by models with a fewer number of skills. Weaker students and schools are best represented, for our data, by models that allow no transfer of knowledge in between skills. Perhaps surprisingly, to accurately predict the level at which a student represents knowledge it is sufficient to know his standardized test score rather than indicators of socio economic status or his school. (Contains 1 figure, 3 tables, and 2 footnotes.) [For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009),&quot; see ED539041.]
Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming
Author: Zafra, Amelia
Date: 2009-07-00
Type: Reports - Research
Subjects: Foreign Countries,Programming,Academic Achievement,Grades (Scholastic),Prediction,Integrated Learning Systems,College Students,College Instruction,Natural Language Processing,Correlation,Open Source Technology,Assignments,Tests,Computer System Design,Computer Managed Instruction,Web Based Instruction,Online Courses,Data,Information Retrieval,Data Analysis,Educational Strategies,Comparative Analysis,Predictor Variables

The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a positive or negative way from the perspective of Multiple Instance Learning (MIL). Computational experiments compare our proposal with the most popular techniques of MIL. Results show that G3P-MI achieves better performance with more accurate models and a better trade-off between such contradictory metrics as sensitivity and specificity. Moreover, it adds comprehensibility to the knowledge discovered and finds interesting relationships that correlate certain tasks and the time devoted to solving exercises with the final marks obtained in the course. (Contains 4 tables.) [For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009),&quot; see ED539041.]
Detecting Symptoms of Low Performance Using Production Rules
Author: Bravo, Javier
Date: 2009-07-00
Type: Reports - Descriptive
Subjects: Data Analysis,Electronic Learning,College Students,Low Achievement,Computer Assisted Testing,Student Evaluation,Mathematics,Programming,Computer Science Education

E-Learning systems offer students innovative and attractive ways of learning through augmentation or substitution of traditional lectures and exercises with online learning material. Such material can be accessed at any time from anywhere using different devices, and can be personalized according to the individual student's needs, goals and knowledge. However, authoring and evaluation of this material remains a complex a task. While many researchers focus on the authoring support, not much has been done to facilitate the evaluation of e-Learning applications, which requires processing of the vast quantity of data generated by students. We address this problem by proposing an approach for detecting potential symptoms of low performance in e-Learning courses. It supports two main steps: generating the production rules of C4.5 algorithm and filtering the most representative rules, which could indicate low performance of students. In addition, the approach has been evaluated on the log files of student activity with two versions of a Web-based quiz system. (Contains 4 tables.) [Funding for this paper was provided by the Spanish Ministry of Science and Education. For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009),&quot; see ED539041.]
Reducing the Knowledge Tracing Space
Author: Ritter, Steven
Date: 2009-07-00
Type: Reports - Evaluative
Subjects: Data Analysis,Intelligent Tutoring Systems,Knowledge Level,Skills,Students,Models,Multivariate Analysis,Mathematics

In Cognitive Tutors, student skill is represented by estimates of student knowledge on various knowledge components. The estimate for each knowledge component is based on a four-parameter model developed by Corbett and Anderson [Nb]. In this paper, we investigate the nature of the parameter space defined by these four parameters by modeling data from over 8000 students in four Cognitive Tutor courses. We conclude that we can drastically reduce the parameter space used to model students without compromising the behavior of the system. Reduction of the parameter space provides great efficiency gains and also assists us in interpreting specific learning and performance parameters. (Contains 4 figures.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]
Using Dirichlet Priors to Improve Model Parameter Plausibility
Author: Rai, Dovan
Date: 2009-07-00
Type: Reports - Descriptive
Subjects: Data Analysis,Statistical Analysis,Probability,Models,Student Behavior,Knowledge Level,Bayesian Statistics,Computation,Comparative Analysis,Students,Grade 8,Urban Schools,Mathematics

Student modeling is a widely used approach to make inference about a student's attributes like knowledge, learning, etc. If we wish to use these models to analyze and better understand student learning there are two problems. First, a model's ability to predict student performance is at best weakly related to the accuracy of any one of its parameters. Second, a commonly used student modeling technique, knowledge tracing, suffers from having multiple sets of parameters providing equally good model fits. Furthermore, common methods for estimating parameters, including conjugate gradient descent and expectation maximization, suffer from finding local maxima that are heavily dependent on their starting values. We propose a technique that estimates Dirichlet priors directly from the data, and show that using those priors produces model parameters that provide a more plausible picture of student knowledge. Although plausibility is difficult to quantify, we employed external measures to show the parameter estimates were indeed improved, even if our model did not predict student behavior any more accurately. (Contains 3 figures and 5 tables.) [Additional funding was provided by the Fulbright Program. For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009),&quot; see ED539041.]
Why, What, and How to Log? Lessons from LISTEN
Author: Mostow, Jack
Date: 2009-07-00
Type: Reports - Descriptive
Subjects: Data Analysis,Data Collection,Intelligent Tutoring Systems,Guidelines,Reading Instruction

The ability to log tutorial interactions in comprehensive, longitudinal, fine-grained detail offers great potential for educational data mining--but what data is logged, and how, can facilitate or impede the realization of that potential. We propose guidelines gleaned over 15 years of logging, exploring, and analyzing millions of events from Project LISTEN's Reading Tutor and its predecessors. (Contains 1 figure.) [For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009),&quot; see ED539041.]
Dimensions of Difficulty in Translating Natural Language into First Order Logic
Author: Barker-Plummer, Dave
Date: 2009-07-00
Type: Reports - Research
Subjects: Data Analysis,Logical Thinking,Difficulty Level,Assignments,Sentences,Translation,Error Patterns,Multivariate Analysis,Matrices

In this paper, we present a study of a large corpus of student logic exercises in which we explore the relationship between two distinct measures of difficulty: the proportion of students whose initial attempt at a given natural language to first-order logic translation is incorrect, and the average number of attempts that are required in order to resolve the error once it has been made. We demonstrate that these measures are broadly correlated, but that certain circumstances can make a hard problem easy to fix, or an easy problem hard to fix. The analysis also reveals some unexpected results in terms of what students find difficult. This has consequences for the delivery of feedback in the Grade Grinder, our automated logic assessment tool; in particular, it suggests we should provide different kinds of assistance depending upon the specific "difficulty profile" of the exercise. (Contains 4 figures and 3 footnotes.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]
A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks
Author: Anaya, Antonio R.
Date: 2009-07-00
Type: Reports - Descriptive
Subjects: Data Analysis,Cooperative Learning,College Students,Adult Students,Distance Education,Electronic Learning,Computer Mediated Communication,Interaction,Multivariate Analysis,Mathematics,Foreign Countries

Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collaboration. We have developed a scalable data mining method, whose objective is to infer information on the collaboration during the collaboration process in a domain-independent way and to improve collaboration process management and learning in an open collaborative educational web environment. Thus, we used statistical indicators of learner's interactions in forums as the data source and a clustering algorithm to classify the data according to learner's collaboration. We showed the information on learner's collaboration to the tutor and learners to help them with collaboration process management. The experimental results support this method. (Contains 1 figure and 2 tables.) [For the complete proceedings, &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009),&quot; see ED539041.]
2010 Kids Count in Colorado! Far from Average: Growing Gaps in Child Well-Being
Author: Colorado Children's Campaign
Date: 2010-00-00
Type: Numerical/Quantitative Data
Subjects: Well Being,Child Health,Poverty,Social Influences,Low Income Groups,Access to Education,Early Childhood Education,Access to Health Care,Counties,Adolescents,Racial Differences,Population Trends,Birth Rate,Family Structure,One Parent Family,Suburbs,Geographic Location,Family Income,At Risk Persons,Early Parenthood,Pregnancy,Suicide,Health Insurance,Obesity,Dental Health,Child Care,Graduation Rate,Kindergarten,Enrollment Trends,Academic Achievement,Dropout Rate

&quot;Kids Count in Colorado!&quot; is an annual publication of the Colorado Children's Campaign, which provides the best available state- and county-level data to measure and track the education, health and general well-being of the state's children. &quot;Kids Count in Colorado!&quot; informs policy debates and community discussions, serving as a valuable resource for policymakers, community leaders, advocates and citizens. This year, the Colorado Children's Campaign celebrates 25 years of serving as the voice for Colorado's kids, using data to identify and advocate for the best policies to improve their lives. In this year's &quot;KIDS COUNT in Colorado!&quot;, the Children's Campaign digs deeper into a troubling trend that emerged in the 2008 and 2009 KIDS COUNT reports. Since 2000, the number of children living in poverty has grown faster in Colorado than in any other state in the nation. At the same time, national reports rank Colorado in the middle--about average--in the nation in child well-being, making the poverty trend even harder to understand. This year's report looks even more closely at the components of the data on poverty, child health, education and early childhood, knowing the key to finding effective solutions lies in the most thorough understanding of the problem. (Contains 46 figures, 4 tables, and 76 footnotes.) [Additional funding was provided by the Gay and Lesbian Fund for Colorado. For the 2009 edition of this report, see ED538954.]
Denver's Public Schools: Reforms, Challenges, and the Future
Author: Bray, Judy
Date: 2009-06-15
Type: Numerical/Quantitative Data
Subjects: Governance,Public Schools,Educational Change,Urban Schools,Educational History,Accountability,Academic Achievement,Merit Pay,Teacher Effectiveness,School Restructuring,Educational Legislation,Federal Legislation,Mathematics Achievement,Reading Achievement,Educational Finance,Educational Innovation,Human Capital,School Districts,Elementary Secondary Education

Denver is currently in the national education spotlight, largely because of its willingness to try a unique combination of major education reforms not seen in other large urban school districts. While many observers hold these reforms in high regard, a steep road lies ahead. Current student results are unacceptable by all measures. The time is right to reflect on the history of Denver's schools, to examine the education results for students in the district, and to trace the reform paths that local leaders have chosen. Then, working together in the months and years ahead, the district along with community and civic leaders, can engage the public to help accelerate the progress of reform. To do so will require that all actors become better informed about DPS reforms and take part in helping to track and achieve high-priority goals. This report is intended to inform and engage the community as it determines how to support its public schools and accelerate reform so that Denver will one day have the schools it deserves. In this paper, three major DPS reforms are profiled including: (1) The Denver Plan; (2) ProComp; and (3) New Schools, Performance and Innovation. If fully implemented as envisioned, these reforms could transform all the education system's functions, from accountability to school governance to funding. Most important, they should improve student achievement in Denver. (Contains 25 figures.) [Additional funding for this project was provided by Qwest Communications.]
Widening the Gap: The Potential Impact of Eliminating the Colorado Preschool Program
Author: Colorado Children's Campaign
Date: 2009-09-00
Type: Reports - Evaluative
Subjects: Academic Achievement,Achievement Gap,Program Effectiveness,Preschool Education,Low Income,Grade 3,School Districts,Preschool Children,Student Evaluation,Nursery Schools

Colorado has seen a growing gap in the achievement of poor and low-income students compared to their more affluent peers. A widening achievement gap has short- and long-term implications for children, as well as the state. Research suggests that high-quality early childhood programs are an effective way to reduce or even eliminate achievement gaps. The Colorado Preschool Program (CPP) is an example of such a program. It has proven to be very effective in closing achievement gaps and preparing children to enter school ready to succeed. This Policy Brief estimates the impact that eliminating CPP would have on the achievement gap. Eliminating CPP would potentially increase the achievement gap between low-income students and their more affluent peers by 3.8 percent to 7.7 percent. The proficiency rates of both low-income and more affluent students would decrease if CPP were eliminated; but because three out of four of the children served by CPP are low-income, the decrease in the percent of students passing the test would be far greater among the state's low-income children. Overall, eliminating CPP could possibly increase the number of third graders that do not reach proficiency on state assessments by more than 3,000. (Contains 4 tables and 10 footnotes.)