ethanporcaro/eric-fulltext-meta huggingface dataset:
Can Typical Behaviors Identified in MOOCs Be Discovered in Other Courses?
Author: An, Truong-Sinh
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Large Group Instruction,Online Courses,Student Behavior,College Students,Programming,Conventional Instruction,Integrated Learning Systems,Educational Technology,Technology Uses in Education,Learning Processes

The emergence of Massive Open Online Courses (MOOCs) has enabled new research to analyze typical behaviors of learners. In this paper, we investigate whether this research is generalizable to other courses that are backed by a learning management system (LMS) as MOOCs are. Building on methods developed by others, we characterize individual learning behaviors in different ways taking into account specificities of the LMS we use. We then apply clustering techniques to uncover typical behaviors in two university courses. One course, JavaFX, teaching about the software programming framework, has been offered as a supplementary online course to students enrolled in an online degree. Enrolling in this course was voluntary and students did not earn any credit towards their degree; in this sense, the JavaFX course bears similarities to a MOOC though it is neither &quot;massive&quot; nor &quot;open to everybody.&quot; The other course is a classical face-to-face course on Advanced Web Technologies (AWT) backed by our LMS; students earn a degree when they pass the final exam. It turns out that the different characterizations of individual learning behaviors are consistent for the JavaFX course and uncover typical behaviors reminiscent of those found by others in MOOCs, while they aren't as applicable to the AWT course. However, typical behaviors found in the AWT course give insights on styles that lead to better marks. [For the full proceedings, see ED596512.]
Epistemic Network Analysis and Topic Modeling for Chat Data from Collaborative Learning Environment
Author: Cai, Zhiqiang
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Epistemology,Network Analysis,Cooperative Learning,Computer Software,Computer Mediated Communication,Scores,Prediction,Learning Processes,Psychology,Introductory Courses,Undergraduate Students,Models,Correlation,Discourse Analysis,Group Dynamics,Computational Linguistics,Student Attitudes

This study investigates a possible way to analyze chat data from collaborative learning environments using epistemic network analysis and topic modeling. A 300-topic general topic model built from TASA (Touchstone Applied Science Associates) corpus was used in this study. 300 topic scores for each of the 15,670 utterances in our chat data were computed. Seven relevant topics were selected based on the total document scores. While the aggregated topic scores had some power in predicting students' learning, using epistemic network analysis enables assessing the data from a different angle. The results showed that the topic score based epistemic networks between low gain students and high gain students were significantly different (?? = 2.00). Overall, the results suggest these two analytical approaches provide complementary information and afford new insights into the processes related to successful collaborative interactions. [For the full proceedings, see ED596512. For the grantee submission, see ED588060.]
Behavior-Based Latent Variable Model for Learner Engagement
Author: Lan, Andrew S.
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Learner Engagement,Student Behavior,Video Technology,Lecture Method,Knowledge Level,Large Group Instruction,Online Courses,Models,Correlation,Tests

We propose a new model for learning that relates video watching behavior and engagement to quiz performance. In our model, a learner's knowledge gain from watching a lecture video is treated as proportional to their latent engagement level, and the learner's engagement is in turn dictated by a set of behavioral features we propose that quantify the learner's interaction with the lecture video. A learner's latent concept knowledge is assumed to dictate their observed performance on in-video quiz questions. One of the advantages of our method for determining engagement is that it can be done entirely within standard online learning platforms, serving as a more universal and less invasive alternative to existing measures of engagement that require the use of external devices. We evaluate our method on a real-world massive open online course (MOOC) dataset, from which we find that it achieves high quality in terms of predicting unobserved first-attempt quiz responses, outperforming two state-of-the-art baseline algorithms on all metrics and dataset partitions tested. We also find that our model enables the identification of key behavioral features (e.g., larger numbers of pauses and rewinds, and smaller numbers of fast forwards) that are correlated with higher learner engagement. [For the full proceedings, see ED596512.]
Characterizing Collaboration in the Pair Program Tracing and Debugging Eye-Tracking Experiment: A Preliminary Analysis
Author: Villamor, Maureen M.
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Programming,Eye Movements,Prior Learning,Computer Assisted Instruction,Educational Technology,Foreign Countries,College Students,Cooperative Learning

This paper characterized the extent of collaboration of pairs of novice programmers as they traced and debugged fragments of code using cross-recurrence quantification analysis (CRQA). This was a preliminary analysis that specifically aimed to compare and assess the collaboration of pairs consisting of two individuals who may have different or same level of prior knowledge given a task. We performed a CRQA to build cross-recurrence plots using eye tracking data and computed for the CRQA metrics, such as recurrence rate (RR), determinism (DET), average diagonal length (L), longest diagonal length (LMAX), entropy (ENTR), and laminarity (LAM) using the CRP toolbox for MATLAB. Results showed that low prior knowledge pairs (BL) collaborated better compared to high prior knowledge pairs (BH) and mixed prior knowledge (M) pairs because of its high RR and DET implying that they had more recurrent fixations and matching scanpaths. However, the BL pairs' high ENTR and LAM could mean that they seemed to have more difficulty in understanding and debugging the programs. All pairs regardless of category had more or less exerted the same level of attunement when asked to debug the programs as evident in their L values. The mixed pairs seemed to have struggled with eye coordination the most as it had the most incidences of low LMAX. [For the full proceedings, see ED596512.]
Clustering Student Sequential Trajectories Using Dynamic Time Warping
Author: Shen, Shitian
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Sequential Approach,Cluster Grouping,Interaction,Student Behavior,Behavior Patterns,Individualized Instruction,Time Perspective,Intelligent Tutoring Systems,Mathematical Logic,Problem Solving,Demonstrations (Educational)

One of the most challenging tasks in the field of Educational Data Mining (EDM) is to cluster students directly based on system-student sequential moment-to-moment interactive trajectories. The objective of this study is to build a general temporal clustering framework that captures the distinct characteristics of students' sequential behaviors patterns, that tracks whether a student's learning experience is &quot;unprofitable,&quot; and can identify such an individual as early as possible so personalized learning can be offered. The central idea of our framework is based on Dynamic Time Warping (DTW), which calculates distance between any two temporal sequences even with different lengths. In this paper, we explore both the original DTW and our proposed normalized DTW to generate distance matrix and apply Hierarchical Clustering to the resulted distance matrix. To fully evaluate the power of our temporal sequential clustering framework, we calculate distance matrix at three types of granularity in the increasing order of: problem, level, and session across three training datasets. As expected, results show that clustering moment-to-moment temporal sequences at problem granularity is more effective than level and session granularity. In addition, our proposed normalized DTW is more effective than both original DTW and the baseline Euclidean distance. [For the full proceedings, see ED596512.]
Convolutional Neural Network for Automatic Detection of Sociomoral Reasoning Level
Author: Tato, Ange
Date: 2017-06-00
Type: Reports - Research
Subjects: Models,Artificial Intelligence,Thinking Skills,Semantics,Sentences,Video Games,Moral Development,Classification,Prediction,Accuracy

We propose a model that employs convolutional neural networks (CNN) to evaluate sociomoral reasoning maturity, a key social ability, necessary for adaptive social functioning. Our model is used in a serious game to evaluate learners. It uses pre-annotated textual data (verbatims) and a coding scheme (SoMoral) applied by experts in psychology. State of the art text classification algorithms (Support Vector Machine, Naïve Bayes, etc.) achieved low results in our context in contrary to the CNN that achieved best results with little fine tuning on the input data representation. We use a simple but efficient input data vectors representation learnt directly from the dataset without loosing the sentences 'semantic'. We present a series of experiments with 5 baseline text classification algorithms and 4 baseline data representation. The results show that our model can predict the level of sociomoral reasoning with about 92% of accuracy. Our findings allow not only to advance the textmining field but also the user modeling in highly social adaptive systems. [For the full proceedings, see ED596512.]
Estimating Individual Treatment Effect from Educational Studies with Residual Counterfactual Networks
Author: Zhao, Siyuan
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Computation,Outcomes of Treatment,Networks,Randomized Controlled Trials,Intervention,Inferences,Educational Experiments,Prediction,Experimental Groups,Control Groups,Models

Personalized learning considers that the causal effects of a studied learning intervention may differ for the individual student. Making the inference about causal effects of studies interventions is a central problem. In this paper we propose the Residual Counterfactual Networks (RCN) for answering counterfactual inference questions, such as &quot;Would this particular student benefit more from the video hint or the text hint when the student cannot solve a problem?&quot;. The model learns a balancing representation of students by minimizing the distance between the distributions of the control and the treated populations, and then uses a residual block to estimate the individual treatment effect based on the representation of the student. We run experiments on semi-simulated datasets and real-world educational online experiment datasets to evaluate the efficacy of our model. The results show that our model matches or outperforms the state-of-the-art. [For the full proceedings, see ED596512.]
Efficient Feature Embeddings for Student Classification with Variational Auto-Encoders
Author: Klingler, Severin
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Classification,Artificial Intelligence,Networks,Learning Disabilities,Prediction,Models,Educational Research,Data Processing,Data Analysis,Intelligent Tutoring Systems

Gathering labeled data in educational data mining (EDM) is a time and cost intensive task. However, the amount of available training data directly influences the quality of predictive models. Unlabeled data, on the other hand, is readily available in high volumes from intelligent tutoring systems and massive open online courses. In this paper, we present a semi-supervised classification pipeline that makes effective use of this unlabeled data to significantly improve model quality. We employ deep variational auto-encoders to learn efficient feature embeddings that improve the performance for standard classifiers by up to 28% compared to completely supervised training. Further, we demonstrate on two independent data sets that our method outperforms previous methods for finding efficient feature embeddings and generalizes better to imbalanced data sets compared to expert features. Our method is data independent and classifier-agnostic, and hence provides the ability to improve performance on a variety of classification tasks in EDM. [For the full proceedings, see ED596512.]
Data-Mining Textual Responses to Uncover Misconception Patterns
Author: Michalenko, Joshua J.
Date: 2017-06-00
Type: Reports - Research
Subjects: Data Analysis,Misconceptions,Student Attitudes,Feedback (Response),Teaching Methods,Educational Quality,Educational Improvement,Guidelines,Classification,Questionnaires,Natural Language Processing,Markov Processes,Monte Carlo Methods,Biology,Science Instruction,Intelligent Tutoring Systems,Advanced Placement,High School Students

An important, yet largely unstudied problem in student data analysis is to detect &quot;misconceptions&quot; from students' responses to &quot;open-response&quot; questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of instruction. In this paper, we propose a new natural language processing-based framework to detect the common misconceptions among students' textual responses to short-answer questions. We propose a probabilistic model for students' textual responses involving misconceptions and experimentally validate it on a real-world student-response dataset. Experimental results show that our proposed framework excels at classifying whether a response exhibits one or more misconceptions. More importantly, it can also automatically detect the common misconceptions exhibited across responses from multiple students to multiple questions; this property is especially important at large scale, since instructors will no longer need to manually specify all possible misconceptions that students might exhibit. [For the full proceedings, see ED596512.]
Combining Machine Learning and Natural Language Processing to Assess Literary Text Comprehension
Author: Balyan, Renu
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Artificial Intelligence,Natural Language Processing,Reading Comprehension,Literature,Comparative Analysis,Accuracy,Classification,Mathematics,Essays,College Students

This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about literary works. Three types of NLP feature sets: unigrams (single content words), elaborative (new) n-grams, and linguistic features were used to classify idea units (paraphrase, text-based inference, interpretive inference). The most accurate classifications emerged using all three NLP features sets in combination, with accuracy ranging from 0.61 to 0.94 (F=0.18 to 0.81). Random Forests, which employs multiple decision trees and a bagging approach, was the most accurate classifier for these data. In contrast, the single classifier, Trees, which tends to &quot;overfit&quot; the data during training, was the least accurate. Ensemble classifiers were generally more accurate than single classifiers. However, Support Vector Machines accuracy was comparable to that of the ensemble classifiers. This is likely due to Support Vector Machines' unique ability to support high dimension feature spaces. The findings suggest that combining the power of NLP and machine learning is an effective means of automating literary text comprehension assessment. [For the full proceedings, see ED596512. For the corresponding grantee submission, see ED577127.]
Incarcerated Students: Encouraging Results from Pilot Program, 2018 Report
Author: Arambula, Raul
Date: 2018-00-00
Type: Reports - Research
Subjects: Community Colleges,Two Year College Students,Institutionalized Persons,Correctional Institutions,Correctional Education,College Programs,Conventional Instruction,Program Evaluation,Instructional Design,Program Effectiveness,Barriers,Educational Finance,Textbooks,Access to Computers,College Applicants,Teacher Recruitment,Scheduling,Pilot Projects,Student Personnel Services,Partnerships in Education

In this report, the California Community Colleges Chancellor's Office (Chancellor's Office) presents its findings, evaluations and analysis of the success and efficacy of the face-to-face community college programs that are being offered to incarcerated students inside California state prisons. The report also discusses barriers and challenges that have been encountered, and provides recommendations for sustainable success. In July 2017, the Board of Governors for California Community Colleges adopted the &quot;Vision for Success&quot; for the 115 California community colleges. With this Vision for Success in mind, the Chancellor's Office has worked collaboratively with the California Department of Corrections and Rehabilitation (CDCR) to design courses and programs utilizing the Guided Pathways framework to improve incarcerated student success outcomes. The Research and Planning Group (RP Group) evaluated these educational programs on their effectiveness in providing face-to-face education at the state prison level. This evaluation included numerous interviews, an online survey and quantitative data analysis of enrollment and success metrics. The evaluation found several issues and potential barriers to the future success of these programs, including: (1) Sustainable funding; (2) Access to textbooks; (3) Access to technology, particularly with paper college applications; (4) Recruitment of faculty; and (5) Problematic scheduling parameters. Despite many academic and personal challenges faced by incarcerated students, they have proven these programs are not only effective, but also highly successful. The Chancellor's Office is committed to the &quot;Vision for Success&quot; and proud that community colleges lead the nation in reaching these new students.
Learner Affect through the Looking Glass: Characterization and Detection of Confusion in Online Courses
Author: Zeng, Ziheng
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Large Group Instruction,Educational Technology,Technology Uses in Education,Group Discussion,Computer Mediated Communication,Affective Behavior,Emotional Response,Models,Prediction,College Students,Data Collection,Data Analysis

Characterizing the nature of students' affective and emotional states and detecting them is of fundamental importance in online course platforms. In this paper, we study this problem by using discussion forum posts derived from large open online courses. We find that posts identified as encoding confusion are actually manifestations of different learner affects pertaining to their informational needs--primarily seeking factual answers. We quantitatively demonstrate that the use of content-related linguistic features and community-related features derived from a post serve as reliable detectors of confusion while widely &quot;outperforming&quot; currently available algorithms of confusion detection. We also point out that several prediction tasks in this domain (e.g., confusion and urgency detection) can be correlated, and that a model trained for one task can effectively be used for making predictions on the other task without requiring labeled examples. Finally, we highlight a very significant problem of adapting the classifier to unseen courses. [For the full proceedings, see ED596512.]
Grouping Students for Maximizing Learning from Peers
Author: Agrawal, Rakesh
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Grouping (Instructional Purposes),Peer Relationship,Interaction,Ability Grouping,Academic Achievement,Scores,Outcomes of Education,Computation,Equations (Mathematics),Educational Strategies,Statistical Distributions

We study the problem of partitioning a class of N students into k groups of n students each (N = k × n), such that their learning from peer interactions is maximized. In our formalization of the problem, any student is able to increase his score in the subject the class is studying up to the score of the student who is at p-percentile among his higher ability peers. In contrast, the past work presumed that only students with score below the group mean may increase their score. We give a partitioning algorithm that maximizes total gain summed over all the students for any value of p such that 100/(100-p) is integer valued. The time complexity of the proposed algorithm is only O(N log N). We also present experimental results using real-life data that show the superiority of the proposed algorithm over current strategies. [For the full proceedings, see ED596512.]
Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning
Author: Wang, Lisa
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Knowledge Level,Pedagogical Content Knowledge,Scaffolding (Teaching Technique),Electronic Learning,Artificial Intelligence,Programming,Models,Coding,Individualized Instruction

Modeling student knowledge while students are acquiring new concepts is a crucial stepping stone towards providing personalized automated feedback at scale. We believe that rich information about a student's learning is captured within her responses to open-ended problems with unbounded solution spaces, such as programming exercises. In addition, sequential snapshots of a student's progress while she is solving a single exercise can provide valuable insights into her learning behavior. Creating representations for a student's knowledge state is a challenging task, but with recent advances in machine learning, there are more promising techniques to learn representations for complex entities. In our work, we feed the embedded program submission sequence into a recurrent neural network and train it on two tasks of predicting the student's future performance. By training on these tasks, the model learns nuanced representations of a student's knowledge, exposes patterns about a student's learning behavior, and reliably predicts future student performance. Even more importantly, the model differentiates within a pool of poorly performing students and picks out students who have true knowledge gaps, giving teachers early warnings to provide assistance. [For the full proceedings, see ED596512.]
Mining Innovative Augmented Graph Grammars for Argument Diagrams through Novelty Selection
Author: Xue, Linting
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Graphs,Visual Aids,Mathematics,Novelty (Stimulus Dimension),Data Analysis,Persuasive Discourse,Artificial Intelligence,Computation

Augmented Graph Grammars are a graph-based rule formalism that supports rich relational structures. They can be used to represent complex social networks, chemical structures, and student-produced argument diagrams for automated analysis or grading. In prior work we have shown that Evolutionary Computation (EC) can be applied to induce empirically-valid grammars for student-produced argument diagrams based upon fitness selection. However this research has shown that while the traditional EC algorithm does converge to an optimal fitness, premature convergence can lead to it getting stuck in local maxima, which may lead to undiscovered rules. In this work, we augmented the standard EC algorithm to induce more heterogeneous Augmented Graph Grammars by replacing the fitness selection with a novelty-based selection mechanism every ten generations. Our results show that this novelty selection increases the diversity of the population and produces better, and more heterogeneous, grammars. [For the full proceedings, see ED596512.]