ethanporcaro/eric-fulltext-meta huggingface dataset:
Investigating Gender Differences on Homework in Middle School Mathematics
Author: Feng, Mingyu
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Gender Differences,Homework,Middle School Students,Grade 7,Educational Technology,Technology Uses in Education,Feedback (Response),Student Records,Standardized Tests,Scores,Help Seeking,Intervention

Recent studies [10, 23] using US nationwide databases showed high school boys spent significantly less time doing homework than girls, based on their responses to questionnaires and surveys. To investigate gender differences in homework in middle school, in this paper, we analyzed computer log data and standardized test scores of more than 1,000 7th grade students who participated in a large-scale randomized controlled online homework efficacy study. Students used the ASSISTments platform to do their homework for a school year. Our results suggested no significant difference between the time the two genders spent on homework overall. There was a marginally significant difference on homework time between genders in the high performing group only. When examining the system-student interaction data, we found significant difference between boys and girls in their helpseeking behaviors. In addition, we found out that boys have benefited from the online homework intervention more than girls. [For the corresponding grantee submission, see ED579217. For the full proceedings, see ED592609.]
Individualizing Bayesian Knowledge Tracing. Are Skill Parameters More Important than Student Parameters?
Author: Yudelson, Michael V.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Bayesian Statistics,Markov Processes,Intelligent Tutoring Systems,Goodness of Fit,Accuracy,Student Evaluation,Hierarchical Linear Modeling

Bayesian Knowledge Tracing (BKT) models were in active use in the Intelligent Tutoring Systems (ITS) field for over 20 years. They have been intensively studied, and a number of useful extensions to them were proposed and experimentally tested. Among the most widely researched extensions to BKT models are various types of individualization. Individualization, broadly defined, is a way to account for variability in students that are working with the ITS that uses BKT model to represent and track student learning. One of the approaches to individualizing BKT is to split its parameters into per-skill and per-student components. In this work, we are proposing an approach to individualizing BKT that is based on Hierarchical Bayesian Models (HBM) and, in addition to capturing student-level variability in the data, weighs the contribution of per-student and per-skill effects to the overall variance in the data. [For the full proceedings, see ED592609.]
Predicting Post-Test Performance from Online Student Behavior: A High School MOOC Case Study
Author: Tomkins, Sabina
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Prediction,Pretests Posttests,Electronic Learning,Student Behavior,High School Students,Online Courses,Academic Achievement,Computer Science Education,Advanced Placement Programs

With the success and proliferation of Massive Open Online Courses (MOOCs) for college curricula, there is demand for adapting this modern mode of education for high school courses. Online and open courses have the potential to fill a much needed gap in high school curricula, especially in fields such as computer science, where there is shortage of trained teachers nationwide. In this paper, we analyze student post-test performance to determine the success of a high school computer science MOOC. We empirically characterize student success by using students' performance on the Advanced Placement (AP) exam, which we treat as a post test. This post-test performance is more indicative of long-term learning than course performance, and allows us to model the extent to which students have internalized course material. Additionally, we analyze and compare the performance of a subset of students who received in-person coaching at their high school, to those students who took the course independently. This comparison provides better understanding of the role of a teacher in a student's learning. We build a predictive machine learning model, and use it to identify the key factors contributing to the success of online high school courses. Our analysis demonstrates that high schoolers can thrive in MOOCs. [For the full proceedings, see ED592609.]
Predicting Student Progress from Peer-Assessment Data
Author: Ashenafi, Michael Mogessie
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Peer Evaluation,Progress Monitoring,Performance,Undergraduate Students,Foreign Countries,Data Analysis,Models,Prediction

Predicting overall student performance and monitoring progress have attracted more attention in the past five years than before. Demographic data, high school grades and test result constitute much of the data used for building prediction models. This study demonstrates how data from a peer-assessment environment can be used to build student progress prediction models. The possibility for automating tasks, coupled with minimal teacher intervention, make peer-assessment an efficient platform for gathering student activity data in a continuous manner. The performances of the prediction models are comparable with those trained using other educational data. Considering the fact that the student performance data do not include any teacher assessments, the results are more than encouraging and shall convince the reader that peer-assessment has yet another advantage to offer in the realm of automated student progress monitoring and supervision. [For the full proceedings, see ED592609.]
LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos
Author: Sharma, Arjun
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Video Technology,Audiovisual Aids,Artificial Intelligence,Prediction,Automation,Classification,Pattern Recognition

Online educational videos have emerged as one of the most popular modes of learning in the recent years. Studies have shown that liveliness is highly correlated to engagement in educational videos. While previous work has focused on feature engineering to estimate liveliness and that too using only the acoustic information, in this paper we propose a technique called LIVELINET that combines audio and visual information to predict liveliness. First, a convolutional neural network is used to predict the visual setup, which in turn identifies the modalities (visual and/or audio) to be used for liveliness prediction. Second, we propose a novel method that uses multimodal deep recurrent neural networks to automatically estimate if an educational video is lively or not. On the StyleX dataset of 450 one-minute long educational video snippets, our approach shows an relative improvement of 7.6% and 1.9% compared to a multimodal baseline and a deep network baseline using only the audio information respectively. [For the full proceedings, see ED592609.]
Predicting Dialogue Acts for Intelligent Virtual Agents with Multimodal Student Interaction Data
Author: Min, Wookhee
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Prediction,Models,Intelligent Tutoring Systems,Computer Simulation,Computer Games,Dialogs (Language),Short Term Memory,Sequential Approach,Accuracy,Design,Recordkeeping,Problem Solving,Role Playing,Middle School Students,Microbiology,Nonverbal Communication,Physiology,Learning Processes,After School Programs,Summer Programs,Decision Making

Recent years have seen a growing interest in intelligent game-based learning environments featuring virtual agents. A key challenge posed by incorporating virtual agents in game-based learning environments is dynamically determining the dialogue moves they should make in order to best support students' problem solving. This paper presents a data-driven modeling approach that uses a Wizard-of-Oz framework to predict human wizards' dialogue acts based on a sequence of multimodal data streams of student interactions with a game-based learning environment. To effectively deal with multiple, parallel sequential data streams, this paper investigates two sequence-labeling techniques: long short-term memory networks (LSTMs) and conditional random fields. We train predictive models utilizing data corpora collected from two Wizard-of-Oz experiments in which a human wizard played the role of the virtual agent unbeknownst to the student. Empirical results suggest that LSTMs that utilize game trace logs and facial action units achieve the highest predictive accuracy. This work can inform the design of intelligent virtual agents that leverage rich multimodal student interaction data in game-based learning environments.
Predicting Performance on MOOC Assessments Using Multi-Regression Models
Author: Ren, Zhiyun
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Large Group Instruction,Online Courses,Educational Technology,Technology Uses in Education,Student Evaluation,Grades (Scholastic),Performance,College Students,Models,Predictor Variables,Multiple Regression Analysis,Data Collection,Study Habits,Learning Processes

The past few years has seen the rapid growth of data mining approaches for the analysis of data obtained from Massive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a student may achieve on a given grade-related assessment based on information, considered as prior performance or prior activity in the course. We develop a personalized linear multiple regression (PLMR) model to predict the grade for a student, prior to attempting the assessment activity. The developed model is real-time and tracks the participation of a student within a MOOC (via click-stream server logs) and predicts the performance of a student on the next assessment within the course offering. We perform a comprehensive set of experiments on data obtained from two openEdX MOOCs via a Stanford University initiative. Our experimental results show the promise of the proposed approach in comparison to baseline approaches and also helps in identification of key features that are associated with the study habits and learning behaviors of students.
How Deep is Knowledge Tracing?
Author: Khajah, Mohammad
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Bayesian Statistics,Data Analysis,Prediction,Intelligent Tutoring Systems,Knowledge Level,Learning Processes,Models,Individual Differences,Cognitive Ability,Retention (Psychology),Inferences,Mathematics Instruction,Elementary School Students,Spanish,Middle School Students,Second Language Learning,Second Language Instruction,Language Tests,Mathematics Tests

In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises--termed &quot;deep knowledge tracing&quot; or &quot;DKT&quot;--has demonstrated a stunning performance advantage over the mainstay of the field, &quot;Bayesian knowledge tracing&quot; or &quot;BKT.&quot; In this article, we attempt to understand the basis for DKT's advantage by considering the sources of statistical regularity in the data that DKT can leverage but which BKT cannot. We hypothesize four forms of regularity that BKT fails to exploit: recency effects, the contextualized trial sequence, inter-skill similarity, and individual variation in ability. We demonstrate that when BKT is extended to allow it more flexibility in modeling statistical regularities--using extensions previously proposed in the literature--BKT achieves a level of performance indistinguishable from that of DKT. We argue that while DKT is a powerful, useful, general-purpose framework for modeling student learning, its gains do not come from the discovery of novel representations--the fundamental advantage of deep learning. To answer the question posed in our title, knowledge tracing may be a domain that does &quot;not&quot; require 'depth'; shallow models like BKT can perform just as well and offer us greater interpretability and explanatory power. [For the full proceedings, see ED592609.]
MOOC Learner Behaviors by Country and Culture; an Exploratory Analysis
Author: Liu, Zhongxiu
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Large Group Instruction,Online Courses,Academic Achievement,Cultural Influences,Cultural Differences,Cultural Awareness,Instructional Design,Educational Technology,Learning Activities,Student Behavior,Student Characteristics,Student Participation,Tests,Friendship,Foreign Countries,Profiles

The advent of Massive Online Open Courses (MOOCs) has led to the availability of large educational datasets collected from diverse international audiences. Little work has been done on the impact of cultural and geographic factors on student performance in MOOCs. In this paper, we analyze national and cultural differences in students' performance in a large-scale MOOC. We situate our analysis in the context of existing theoretical frameworks for cultural analysis. We focus on three dimensions of learner behavior: course activity profiles; quiz activity profiles; and most connected forum peer or &quot;best friends.&quot; We conclude that countries or associated cultural clusters are associated with differences in all three dimensions. These findings stress the need for more research on the internationalization in online education and greater intercultural awareness among MOOC designers.
Personalization of Learning Paths in Online Communities of Creators
Author: Sun, Mingxuan
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Communities of Practice,Electronic Learning,Technology Uses in Education,Learning Processes,Difficulty Level,Information Systems,Decision Making

In massive online communities of creators (OCOCs), one of the core challenges is to encourage users to learn to create original contents using basic components. Recommending the right learning components at the right time is critical for improving user engagement and has not been fully studied due to the unstructured nature of online communities. To address the problem, we propose in this paper a novel recommendation model which integrates Cox's survival analysis and collaborative filtering. Our model can incorporate factors such as user learning history and social engagements, which provides us insights in improving the personalized service. We apply our method to the user data from Scratch online platform and demonstrate the performance of the model. [For the full proceedings, see ED592609.]
Modelling the Way: Using Action Sequence Archetypes to Differentiate Learning Pathways from Learning Outcomes
Author: Ng, Kelvin H. R.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Word Problems (Mathematics),Mathematics Instruction,Arithmetic,Problem Solving,Models,Classification,Elementary School Students,Student Behavior,Behavior Patterns,Computer Uses in Education,Prediction,Automation,Sequential Learning,Foreign Countries,Grade 2,Data Analysis,Persistence

During the semester break, 36 second-grade students accessed a set of resources and completed a series of online math activities focused on the application of the model method for arithmetic in two contexts 1) addition/subtraction and 2) multiplication/division. The learning environment first modeled and then supported the use of a scripted series of steps for solving mathematical word problems. As students completed the activities, the learning environment captured their event-related data. We then used a combination of Affinity Propagation, an automated form of clustering, and sequential pattern mining to convert the activity logs into interpretable activity sequences. Analysis of the activity sequences identified distinct patterns of behavior that strongly predicted which students would transit from the familiar addition/subtraction word problem activity to the unfamiliar multiplication/division word problem activity. Students who showed the greatest and least compliance with the script were the least likely to attempt the multiplication/division activity. Students who showed more of a schematic problem solving process were more likely to continue to the multiplication/division activity.
Modeling Visitor Behavior in a Game-Based Engineering Museum Exhibit with Hidden Markov Models
Author: Tissenbaum, Mike
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Museums,Exhibits,Behavior,Markov Processes,Discovery Learning,Engineering Education,Nonschool Educational Programs,Automation,Identification

Research has shown that supporting tinkering and exploration promotes a wide range of STEM related literacies. However, the open-endedness of tinkering environments makes it difficult to know whether learners' exploration is productive or not. This is especially true in museum spaces, where dwell times are short and facilitators lack a history of engagement with individual visitors. In response, this study uses telemetry data from Oztoc -- an open-ended exploratory tabletop exhibit in which visitors embody the roles of engineers who are tasked with attracting and cataloging newly discovered aquatic creatures by building working electronic circuits. This data is used to build Hidden Markov Models (HMMs) to devise an automated scheme of identifying when a visitor is behaving productively or unproductively. Evaluation of our HMM was shown to effectively discern when visitors were productively and unproductively engaging with the exhibit. Using a Markov model, we identify common patterns of visitor movement from unproductive to productive states to shed light on how visitors struggle and the moves they made to overcome these struggles. These findings offer considerable promise for understanding how learners productively and unproductively persevere in open-ended exploratory environments and the potential for developing real time supports to help facilitators know how and when to best engage with visitors.
Properties and Applications of Wrong Answers in Online Educational Systems
Author: Pelánek, Radek
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Foreign Countries,Online Systems,Geography,Anatomy,Arithmetic,Comparative Analysis,Graphs,Data Analysis,Classification,Misconceptions,Error Patterns,Elementary Secondary Education,Feedback (Response),Computer Assisted Testing,Test Preparation,Test Items,Item Analysis,Mathematics Instruction,Maps,Computer Software

In online educational systems we can easily collect and analyze extensive data about student learning. Current practice, however, focuses only on some aspects of these data, particularly on correctness of students answers. When a student answers incorrectly, the submitted wrong answer can give us valuable information. We provide an overview of possible applications of wrong answers and analyze wrong answers from three different educational systems (geography, anatomy, basic arithmetic). Using this cross-system comparison we illustrate some common properties of wrong answers. We also propose techniques for processing of wrong answers and their visualization, particularly an approach to item clustering based on community detection in a confusion graph.
On Competition for Undergraduate Co-Op Placements: A Graph Mining Approach
Author: Jiang, Yuheng
Date: 2016-00-00
Type: Reports - Research
Subjects: Undergraduate Students,Student Placement,Cooperative Education,Research Methodology,Data Collection,Graphs,Employment Interviews,College Programs,Competition,Student Employment,Job Placement,Case Studies,Interdisciplinary Approach

We propose a graph mining methodology to analyze the relationships among academic programs from the point of view of cooperative education. The input consists of student - job interview pairs, with each student labelled with his or her academic program. From this input, we build a weighted directed graph, which we refer to as a program graph, in which vertices correspond to academic programs and edge weights denote the percentage of jobs that interviewed at least one student from both programs. We show that various properties of this graph have natural interpretations in terms of the relationships among academic programs and competition for co-op jobs. We also present a case study that illustrates the utility of the proposed methodology. [For the full proceedings, see ED592609.]
Learning Curves for Problems with Multiple Knowledge Components
Author: an de Sande, Brett
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Learning Processes,Knowledge Level,Problem Solving,Homework,Technology Uses in Education,Intelligent Tutoring Systems,Measurement,Mastery Learning,Computation,Maximum Likelihood Statistics

Learning curves have proven to be a useful tool for understanding how a student learns a given skill as they progress through a curriculum. A learning curve for a given Knowledge Component (KC) is a plot of some measure of competence as a function of the number of opportunities the student has had to apply that KC. Consider the case where each problem-solving step is recorded by, for instance, by an intelligent tutoring system. In this case, one normally assigns a unique KC to each problem-solving step and the construction of the associated learning curves is straightforward. On the other hand, many online homework systems only evaluate the student's final answer to a problem. In that case, the student has generally applied a number of KCs to find the answer and their performance on the problem is some composite of their mastery of all of the requisite KCs. In this paper, we propose a simple method for generating learning curves for multipleKC problems that is independent of any particular theory of learning. In the case where there is only one KC per problem, the method reduces to the ordinary learning curves. We demonstrate this method using a set of artificially generated student data. [For the full proceedings, see ED592609.]
How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations
Author: Rau, Martina A.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Models,Learning Processes,STEM Education,Concept Formation,Nonverbal Ability,Intervention,Teaching Methods,Computer Software,Visual Stimuli,Visual Perception,Undergraduate Students,Molecular Structure,Science Instruction,Chemistry,Accuracy,Online Systems

To succeed in STEM, students need to learn to use visual representations. Most prior research has focused on conceptual knowledge about visual representations that is acquired via verbally mediated forms of learning. However, students also need perceptual fluency: the ability to rapidly and effortlessly translate among representations. Perceptual fluency is acquired via nonverbal, implicit learning processes. A challenge for instructional interventions that focus on implicit learning is to model students' knowledge acquisition. Because implicit learning is non-verbal, we cannot rely on traditional methods, such as expert interviews or student think-alouds. This paper uses similarity learning, a machine learning method that can assess how people perceive similarity between visual representations. We used this approach to model how undergraduate students perceive similarity between visual representations of chemical molecules. The approach achieved good accuracy in predicting students' similarity judgments and expands expert predictions of how students might perceive visual representations of molecules. [For the full proceedings, see ED592609.]