ethanporcaro/eric-fulltext-meta huggingface dataset:
Developing Computational Methods to Measure and Track Learners&apos; Spatial Reasoning in an Open-Ended Simulation
Author: Mallavarapu, Aditi
Date: 2015-00-00
Type: Journal Articles
Subjects: Spatial Ability,Urban Areas,Neighborhoods,Conservation (Environment),Data Analysis,Environmental Education,Teaching Methods,Natural Disasters,Computer Assisted Instruction,Computer Simulation,Feedback (Response),Formative Evaluation,Problem Based Learning,High School Students,Undergraduate Students,Regression (Statistics),Instructional Effectiveness,Statistical Analysis,Monte Carlo Methods,Multivariate Analysis

Interactive learning environments can provide learners with opportunities to explore rich, real-world problem spaces, but the nature of these problem spaces can make assessing learner progress difficult. Such assessment can be useful for providing formative and summative feedback to the learners, to educators, and to the designers of the environments. This work adds to a growing body of research that is applying EDM techniques to more open-ended problem spaces. The open-ended problem space under study here was an environmental science simulation. Learners were confronted with the real-world challenge of effectively placing green infrastructure in an urban neighborhood to reduce surface flooding. Learners could try out different 2D spatial arrangements of green infrastructure and use the simulation to test each solution's impact on flooding. The learners' solutions and the solutions' performances were logged during a controlled experiment with different user interface designs for the simulation. As with many open-problem spaces, analyzing this data was difficult due to the large state space, many good solutions, and many alternate paths to those good solutions. This work proposes a procedure for reducing the state space of solutions defined by 2D spatial patterns while maintaining their critical spatial properties. Spatial reasoning problems are a problem class not extensively examined by EDM, so this work sets the stage for further research in this area. This work also details a procedure for discovering effective 2D spatial strategies and solution paths, demonstrates how this information can be used to give formative feedback to the designers of the interactive learning environment, and speculates about how it could be used to provide formative feedback to learners.
Move Your Lamp Post: Recent Data Reflects Learner Knowledge Better than Older Data
Author: Galyardt, April
Date: 2015-00-00
Type: Journal Articles
Subjects: Achievement Rating,Performance Based Assessment,Bayesian Statistics,Data Analysis,Data Interpretation,Data Processing,Models,Educational Practices,Mathematical Models,Problem Sets,Skill Analysis,Comparative Analysis,Simulation,Student Behavior,Markov Processes

In educational technology and learning sciences, there are multiple uses for a predictive model of whether a student will perform a task correctly or not. For example, an intelligent tutoring system may use such a model to estimate whether or not a student has mastered a skill. We analyze the significance of data recency in making such predictions, i.e., asking whether relatively more recent observations of a student's performance matter more than relatively older observations. We investigate several representations of recency, such as the count of prior practice in the AFM model, and the proportion of recent successes with exponential and box kernels. We find that an exponential decay of a proportion of successes provides the summary of recent practice with the highest predictive accuracy over alternative models. As a secondary contribution, we develop a new logistic regression model, Recent-Performance Factors Analysis that leverages this representation of recent performance, and has higher predictive accuracy than existing logistic regression models.
Analysing Student Performance Using Sparse Data of Core Bachelor Courses
Author: Saarela, Mirka
Date: 2015-00-00
Type: Journal Articles
Subjects: Data Analysis,Academic Achievement,Undergraduate Students,Core Curriculum,Computer Science Education,College Curriculum,Bachelors Degrees,Required Courses,Grades (Scholastic),Correlation,Multivariate Analysis,Robustness (Statistics),Generalization,Regression (Statistics),Mixed Methods Research,Prediction

Curricula for Computer Science (CS) degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separate CS core studies, the learning skills for these and other required courses may vary a lot, which is shown in students' overall performance. To analyze this situation, we apply nonstandard educational data mining techniques on a preprocessed log file of the passed courses. The joint variation in the course grades is studied through correlation analysis while intrinsic groups of students are created and analyzed using a robust clustering technique. Since not all students attended all courses, there is a nonstructured sparsity pattern to cope with. Finally, multilayer perceptron neural network with cross-validation based generalization assurance is trained and analyzed using analytic mean sensitivity to explain the nonlinear regression model constructed. Local (within-methods) and global (between-methods) triangulation of different analysis methods is argued to improve the technical soundness of the presented approaches, giving more confidence to our final conclusion that general learning capabilities predict the students' success better than specific IT skills learned as part of the core studies.
Analyzing Process Data from Game/Scenario-Based Tasks: An Edit Distance Approach
Author: Hao, Jiangang
Date: 2015-00-00
Type: Journal Articles
Subjects: Task Analysis,Data Analysis,Vignettes,Correlation,Scoring Rubrics,Comparative Analysis,Student Evaluation,Scores,National Competency Tests,Engineering,Literacy,Responses,Computer Games,Engineering Education,Computer Assisted Instruction,Factor Analysis

Students' activities in game/scenario-based tasks (G/SBTs) can be characterized by a sequence of time-stamped actions of different types with different attributes. For a subset of G/SBTs in which only the order of the actions is of great interest, the process data can be well characterized as a string of characters (i.e., action string) if we encode each action name as a single character. In this article, we report our work on evaluating students' performances by comparing how far their action strings are from the action string that corresponds to the best performance, where the proximity is quantified by the edit distance between the strings. Specifically, we choose the Levenshtein distance, which is defined as the minimum number of insertions, deletions, and replacements needed to convert one character string into another. Our results show a strong correlation between the edit distances and the scores obtained from the scoring rubrics of the pump repair task from the National Assessment of Education Progress Technology and Engineering Literacy assessments, implying that the edit distance to the best performance sequence can be considered as a new feature variable that encodes information about students' proficiency, which sheds light on the value of data-driven scoring rules for test and task development and for refining the scoring rubrics.
Understanding Student Language: An Unsupervised Dialogue Act Classification Approach
Author: Ezen-Can, Aysu
Date: 2015-00-00
Type: Journal Articles
Subjects: Classification,Dialogs (Language),Computational Linguistics,Information Retrieval,Markov Processes,Natural Language Processing,Introductory Courses,Computer Science Education,Data Analysis,Intelligent Tutoring Systems,Cooperative Learning,Classroom Communication,Computer Mediated Communication,Accuracy,Recall (Psychology),Teacher Student Relationship,Higher Education,Qualitative Research,Statistical Analysis

Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language understanding of educational dialogues. Automatic dialogue act modeling has long been addressed with supervised classification techniques that require substantial manual time and effort. Recently, there is emerging interest in unsupervised dialogue act classification, which addresses the challenges related to manually labeling corpora. This paper builds on the growing body of work in unsupervised dialogue act classification and reports on the novel application of an information retrieval technique, the Markov Random Field, for the task of unsupervised dialogue act classification. Evaluation against manually labeled dialogue acts on a tutorial dialogue corpus in the domain of introductory computer science demonstrates that the proposed technique outperforms existing approaches to education-centered unsupervised dialogue act classification. Unsupervised dialogue act classification techniques have broad application in educational data mining in areas such as collaborative learning, online message boards, classroom discourse, and intelligent tutoring systems.
A Model-Based Approach to Predicting Graduate-Level Performance Using Indicators of Undergraduate-Level Performance
Author: Zimmermann, Judith
Date: 2015-00-00
Type: Journal Articles
Subjects: Undergraduate Students,Graduate Students,Academic Achievement,Prediction,Models,Computer Science Education,Regression (Statistics),Sampling,Statistical Inference,Data Analysis,Grade Point Average,College Admission,Foreign Countries,Statistical Analysis

The graduate admissions process is crucial for controlling the quality of higher education, yet, rules-of-thumb and domain-specific experiences often dominate evidence-based approaches. The goal of the present study is to dissect the predictive power of undergraduate performance indicators and their aggregates. We analyze 81 variables in 171 student records from a Bachelor's and a Master's program in Computer Science and employ state-of-the-art methods suitable for high-dimensional data-settings. We consider regression models in combination with variable selection and variable aggregation embedded in a double-layered cross-validation loop. Moreover, bootstrapping is employed to identify the importance of explanatory variables. Critically, the data is not confounded by an admission-induced selection bias, which allows us to obtain an unbiased estimate of the predictive value of undergraduate-level indicators for subsequent performance at the graduate level. Our results show that undergraduate-level performance can explain 54% of the variance in graduate-level performance. Significantly, we unexpectedly identified the third-year grade point average as the most significant explanatory variable, whose influence exceeds the one of grades earned in challenging first-year courses. Analyzing the structure of the undergraduate program shows that it primarily assesses a single set of student abilities. Finally, our results provide a methodological basis for deriving principled guidelines for admissions committees.
Personalisation of Generic Library Search Results Using Student Enrolment Information
Author: Alaofi, Marwah
Date: 2015-00-00
Type: Journal Articles
Subjects: Information Retrieval,Electronic Libraries,Online Searching,Enrollment,Student Needs,Search Strategies,Profiles,Scores,Graduate Students,Information Technology,Foreign Countries,Masters Programs,Program Effectiveness,College Libraries,Information Needs

This research explores the application of implicit personalisation techniques in information retrieval in the context of education. Motivated by the large and ever-growing volume of resources in digital libraries, coupled with students' limited experience in searching for these resources, particularly in translating their information needs into queries, this research investigates the potential of incorporating student enrolment information, that is, published information on the units/subjects they are enrolled in, to identify students' learning needs and produce personalised search results. We propose, implement, and evaluate a personalisation approach that makes use of the collection of units a student is enrolled in to generate a student profile used to estimate the relevance of the library resources. To do this, we propose the use of a &quot;Final Relevance Score&quot; (&quot;FRS&quot;) measure, which assigns a relevance score for each query-dependent resource based on its similarity to both the student profile and the submitted query, with a parameter controlling the effect of both. To examine the effectiveness of this approach and whether it truly produces any improvement over the library generic approach, this approach was translated into an application called PersoLib and evaluated by a group of 16 students who were doing foundation units in the Masters of Information Technology course at Monash University. Masters of Information Technology course at Monash University. The evaluation results show that the personalisation approach significantly outperforms the library generic approach. This shows the potential of incorporating student enrolment information to create a more effective search environment in which students' search results are not only driven by the submitted query, but also by the units they are enrolled in.
Of Needles and Haystacks: Building an Accurate Statewide Dropout Early Warning System in Wisconsin
Author: Knowles, Jared E.
Date: 2015-00-00
Type: Journal Articles
Subjects: Dropouts,Models,Prediction,Risk,Graduation Rate,High School Graduates,Statistical Analysis,Middle School Students,Data Collection,Classification,Computer Software,Open Source Technology,Regression (Statistics),Comparative Analysis

The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use statewide and currently provides predictions on the likelihood of graduation for over 225,000 students. DEWS represents a novel statistical learning based approach to the challenge of assessing the risk of non-graduation for students and provides highly accurate predictions for students in the middle grades without expanding beyond mandated administrative data collections. Similar dropout early warning systems are in place in many jurisdictions across the country. Prior research has shown that in many cases the indicators used by such systems do a poor job of balancing the trade off between correct classification of likely dropouts and false-alarm (Bowers et al., 2013). Building on this work, DEWS uses the receiver-operating characteristic (ROC) metric to identify the best possible set of statistical models for making predictions about individual students. This paper describes the DEWS approach and the software behind it, which leverages the open source statistical language R (R Core Team, 2013). As a result DEWS is a flexible series of software modules that can adapt to new data, new algorithms, and new outcome variables to not only predict dropout, but also impute key predictors as well. The design and implementation of each of these modules is described in detail as well as the open-source R package, EWStools, that serves as the core of DEWS (Knowles, 2014).
Unraveling Students&apos; Interaction around a Tangible Interface Using Multimodal Learning Analytics
Author: Schneider, Bertrand
Date: 2015-00-00
Type: Journal Articles
Subjects: Educational Research,Data Collection,Data Analysis,Educational Environment,Information Retrieval,Computer Interfaces,Cooperative Learning,Man Machine Systems,Interaction,Multivariate Analysis,Statistical Analysis

In this paper, we describe multimodal learning analytics (MMLA) techniques to analyze data collected around an interactive learning environment. In a previous study (Schneider &amp; Blikstein, submitted), we designed and evaluated a Tangible User Interface (TUI) where dyads of students were asked to learn about the human hearing system by reconstructing it. In the current study, we present the analysis of the data collected in the form of logs, both from students' interaction with the tangible interface and as well as from their gestures, and we describe how we extracted meaningful predictors for student learning from these two datasets. First we show how Natural Language Processing (NLP) techniques can be used on the tangible interface logs to predict learning gains. Second, we explored how Kinect™ data can inform &quot;in-situ&quot; interactions around a tabletop by using clustering algorithms to find prototypical body positions. Finally, we fed those features to a machine-learning classifier (Support Vector Machine) and divided students in two groups after performing a median split on their learning scores. We found that we were able to predict students' learning gains (i.e. being above or belong the median split) with very high accuracy. We discuss the implications of these results for analyzing rich data from multimodal learning environments.
A Comparison of Educational Statistics and Data Mining Approaches to Identify Characteristics That Impact Online Learning
Author: Miller, L. Dee
Date: 2015-00-00
Type: Journal Articles
Subjects: Comparative Analysis,Data Analysis,Hierarchical Linear Modeling,Electronic Learning,Undergraduate Students,Computer Science Education,Outcomes of Education,Predictor Variables,Resource Units,Learning Strategies,Questionnaires,Accuracy,Prediction,Correlation,Likert Scales

Learning objects (LOs) are important online resources for both learners and instructors and usage for LOs is growing. Automatic LO tracking collects large amounts of metadata about individual students as well as data aggregated across courses, learning objects, and other demographic characteristics (e.g. gender). The challenge becomes identifying which of the many variables derived from tracked data are useful for predicting student learning. This challenge has prompted considerable research in the field of educational data mining and learning analytics. This work advances such research in four ways. First, we bring together two approaches for finding salient variables from separate research areas: hierarchical linear modeling (HLM) from education and Lasso feature selection from computer science. Second, we show that these two approaches have complimentary and synergistic results with some variables considers salient by both and others salient by only one. Third, and most importantly, we demonstrate the benefits of a combined approach that considers a variable salient when either HLM or Lasso consider that variable salient. This combined approach both improves model predictive accuracy and finds additional variables considered salient in previous datasets on student learning. Lastly, we use the results to provide insights into the salient variables to the learning outcome in undergraduate CS education. Overall, this work suggests a combined approach that improves the identification of salient variables in big data and also improves the design of LO tracking systems for learning management systems.
Using Data Mining Results to Improve Educational Video Game Design
Author: Kerr, Deirdre
Date: 2015-00-00
Type: Journal Articles
Subjects: Information Retrieval,Data Analysis,Video Games,Educational Games,Multivariate Analysis,Mathematics Skills,Problem Solving,Design,Fractions,Pretests Posttests,Grade 6,Student Attitudes

This study uses information about in-game strategy use, identified through cluster analysis of actions in an educational video game, to make data-driven modifications to the game in order to reduce construct-irrelevant behavior. The examination of student strategies identified through cluster analysis indicated that (a) it was common for students to pass certain levels using incorrect mathematical strategies and (b) throughout the game a large number of students used order-based strategies to solve problems rather than strategies based on mathematics, making measurement of their mathematical ability difficult. To address the construct irrelevant variance produced by these issues, two minor changes were made to the game and students were randomly assigned to either the original version or the revised version. Students who played the revised version (a) solved levels using incorrect mathematical strategies significantly less often and (b) used order-based strategies significantly less often than students who played the original version. Additionally, student perception of the revised version of the game was more positive than student perception of the original version, though there were no significant differences in either in-game or paper-and-pencil posttest performance. These findings indicate that data mining results can be used to make targeted modifications to a game that increased the interpretability of the resulting data without negatively impacting student perception or performance.
Cultivating a Value for Non-Human Interests through the Convergence of Animal Welfare, Animal Rights, and Deep Ecology in Environmental Education
Author: Kopnina, Helen
Date: 2015-12-00
Type: Journal Articles
Subjects: Environmental Education,Sustainable Development,Natural Resources,Animals,Ecology,Ethics,Sustainability,Moral Values,Democratic Values,Social Values

While the original objective of environmental education (EE) and education for sustainable development (ESD) acquired an awareness of the natural world and its current plight, animal welfare (AW), animal rights (AR), and deep ecology (DE) have often been absent within EE and ESD. AW and AR focus their attention on individual animals, while the DE perspective recognizes the intrinsic value of the environment. In this article, we shall discuss how the integration of these three approaches within EE/ESD can and should be improved, with particular reference to the ethical underpinnings of educational scholarship and practice. This article will argue that these three positions are well placed to enhance the democratic practices of EE/ESD through the adoption of an inclusive pluralism that embraces representation of non-human species and recognizes their interests.
Reading Comprehension Instruction for Adolescents with Learning Disabilities: A Reality Check
Author: Ko, Tiffany
Date: 2015-12-00
Type: Journal Articles
Subjects: Reading Comprehension,Disabilities,Adolescents,Learning Disabilities,Reading Instruction,Special Education Teachers,Observation,Interviews,High School Students,Secondary School Teachers,Teaching Methods,Oral Reading,Questioning Techniques,Learning Activities,Prior Learning,Instructional Materials,Urban Schools,Program Effectiveness

Reading comprehension is a significant concern for adolescents with learning disabilities (LD), particularly in secondary schools in the United States (US) where content is taught primarily through textbooks. Surprisingly little is known about the actual reading instruction for students with LD in secondary classrooms. Thus, the purpose of this study was to examine the reading comprehension instruction in US secondary special education classrooms. Eight special education teachers in urban high schools were observed and interviewed. Findings showed that teachers implemented a number of reading comprehension practices, not all were considered &quot;best practice&quot;. The most frequently observed practices included reading aloud, questioning, seatwork, activating prior knowledge, and using graphic organizers. Explicit instruction in how and when to use reading comprehension strategies, however, was not observed. This study reveals the extent to which evidence-based reading comprehension practices are not making their way into secondary reading classrooms and offers insight into factors that teachers state as influencing their instruction for students with LD. Contains a list of references and notes.