ethanporcaro/eric-fulltext-meta huggingface dataset:
A Comparison of Features for the Automatic Labeling of Student Answers to Open-Ended Questions
Author: Mantecon, Jesus Gerardo Alvarado
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Questioning Techniques,Comparative Analysis,Models,Semantics,Health Education,Scores,Labeling (of Persons)

The automatic evaluation of text-based assessment items, such as short answers or essays, is an open and important research challenge. In this paper, we compare several features for the classification of short open-ended responses to questions related to a large first-year health sciences course. These features include a) traditional n-gram models; b) entity URIs (Uniform Resource Identifier) and c) entity mentions extracted using a semantic annotation API; d) entity mention embeddings based on GloVe, and e) entity URI embeddings extracted from Wikipedia. These features are used in combination with classification algorithms to discriminate correct answers from incorrect ones. Our results show that, on average, n-gram features performed the best in terms of precision and entity mentions in terms of f1-score. Similarly, in terms of accuracy, entity mentions and n-gram features performed the best. Finally, features based on dense vector representations such as entity embeddings and mention embeddings obtained the best f1-score for predicting correct answers. [For the full proceedings, see ED593090.]
Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams
Author: Chen, Weiyu
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Behavioral Science Research,Measures (Individuals),Online Courses,Prerequisites,Knowledge Level,Models,Course Content,Prediction,Networks

Knowledge of prerequisite dependencies is crucial to several aspects of learning, from the organization of learning content to the selection of personalized remediation or enrichment for each learner. As the amount of content is scaled up, however, it becomes increasingly difficult to manually specify all of the prerequisites among the different content parts, necessitating automation. Since existing approaches to automatically inferring prerequisite dependencies rely on analysis of content (e.g., topic modeling of text) or performance (e.g., quiz results tied to content) data, they are not feasible in cases where courses have no assessments or only short content pieces (e.g., short video segments). In this paper, we propose an algorithm that extracts prerequisite information using learner behavioral data instead of content and performance data, and apply it to an online short course. By modeling learner interaction with course content through a recurrent neural network-based architecture, our algorithm characterizes the prerequisite structure as latent variables, and estimates them from learner behavior. Through evaluation on a dataset of roughly 12,000 learners in a course we hosted on our platform, we show that our algorithm excels at both predicting behavior and revealing fine-granular insights into prerequisite dependencies between content segments, with validation provided by a course administrator. Our approach of content analytics using large-scale behavioral data complements existing approaches that focus on course content and/or performance data. [For the full proceedings, see ED593090.]
An Open Vocabulary Approach for Estimating Teacher Use of Authentic Questions in Classroom Discourse
Author: Cook, Connor
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Vocabulary,Academic Discourse,Teacher Effectiveness,Partnerships in Education,Observation,Teaching Methods,Correlation,English Instruction,Language Arts,English Teachers,Grade 7,Grade 8,Middle School Students,Middle School Teachers,Questioning Techniques

Automatic assessment of the quality of classroom discourse can have a transformative effect on research and practice on improving teaching effectiveness. We improve on a previous automated method to measure teacher authentic questions -- open-ended questions without pre-scripted responses that predict student achievement growth -- using classroom audio and expert question codes from two sources: (1) a large archival database of text transcripts of 428 class-sessions from 116 classrooms, and (2) a newly collected sample of 132 high-quality audio recordings with automatic speech recognition transcripts from 27 classrooms. Whereas previous work utilized a &quot;closed vocabulary&quot; approach, consisting of 732 pre-defined word, sentence, and discourse level features, the present &quot;open vocabulary&quot; approach exclusively utilized word and phrase counts from the transcripts themselves. The two approaches yielded substantial, but statistically equivalent, correlations with gold-standard human codes of authenticity (Pearson r's of 0.396 vs. 0.424 and 0.602 vs. 0.613 for datasets 1 and 2, respectively). Importantly, averaging estimates from the two approaches resulted in statistically significant improvements over either approach (r's of 0.492 and 0.686 for datasets 1 and 2, respectively). We discuss implications of our findings for automated analysis of classroom discourse. [For the full proceedings, see ED593090.]
Document Chunking and Learning Objective Generation for Instruction Design
Author: Tran, Khoi-Nguyen
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Behavioral Objectives,Instructional Design,Reference Materials,Prediction,Verbs,Computational Linguistics,Teaching Methods,Semantics,Banking,Pharmacology

Instructional Systems Design is the practice of creating of instructional experiences that make the acquisition of knowledge and skill more efficient, effective, and appealing [18]. Specifically in designing courses, an hour of training material can require between 30 to 500 hours of effort in sourcing and organizing reference data for use in just the preparation of course material. In this paper, we present the first system of its kind that helps reduce the effort associated with sourcing reference material and course creation. We present algorithms for document chunking and automatic generation of learning objectives from content, creating descriptive content metadata to improve content-discoverability. Unlike existing methods, the learning objectives generated by our system incorporate pedagogically motivated Bloom's verbs. We demonstrate the usefulness of our methods using real world data from the banking industry and through a live deployment at a large pharmaceutical company. [For the full proceedings, see ED593090.]
Services for Students with Disabilities at Higher Education of Pakistan
Author: Zahid, Gulnaz
Date: 2018-00-00
Type: Journal Articles
Subjects: Disabilities,Higher Education,Foreign Countries,College Faculty,Teacher Attitudes,Inclusion,Teacher Student Relationship,Teaching Experience,Special Needs Students,College Students

This study is about the support and services provided to the students with disabilities in higher education of Pakistan. One university of Islamabad with a national and international repute was selected as a case study. Twelve focus group discussions were carried out with 100 faculty members. The thematic analysis was done that helped to understand university teachers' perceptions and experiences relating to facilitating students with disability. Major theme identified include understanding the concept of disability, services available to students, faculty members' practices and need for a specified disability center. Recommendations are derived for higher education in Pakistan.
Studying Affect Dynamics and Chronometry Using Sensor-Free Detectors
Author: Botelho, Anthony F.
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Psychological Patterns,Time,Computer Assisted Instruction,Affective Behavior,Student Behavior,Middle School Students,Identification,Measurement Techniques

Student affect has been found to correlate with short- and long-term learning outcomes, including college attendance as well as interest and involvement in Science, Technology, Engineering, and Mathematics (STEM) careers. However, there still remain significant questions about the processes by which affect shifts and develops during the learning process. Much of this research can be split into affect dynamics, the study of the temporal transitions between affective states, and affective chronometry, the study of how an affect state emerges and dissipates over time. Thus far, these affective processes have been primarily studied using field observations, sensors, or student self-report measures; however, these approaches can be coarse, and obtaining finer-grained data produces challenges to data fidelity. Recent developments in sensor-free detectors of student affect, utilizing only the data from student interactions with a computer-based learning platform, open an opportunity to study affect dynamics and chronometry at moment-to-moment levels of granularity. This work presents a novel approach, applying sensor-free detectors to study these two prominent problems in affective research. [For the full proceedings, see ED593090.]
Student Performance Prediction by Discovering Inter-Activity Relations
Author: Sahebi, Shaghayegh
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Performance,Prediction,Measurement Techniques,Learning Activities,Correlation,Educational Resources,Interaction,Educational Technology,Technology Uses in Education

Performance prediction has emerged as one of the most popular approaches to leverage large volume of online learning data. In the majority of current works, performance prediction is based on students' past activities in graded learning resources (such as problems and quizzes), while their activities in non-graded resources (such as reading material) are ignored. In this paper, we introduce an approach that can take advantage of students' work with non-graded learning resources, as &quot;auxiliary&quot; data, in order to predict students' performance in graded resources. This approach can discover the hidden inter-relationships between learning resources of different types, only using student activity data. Based on our experiments, the proposed approach can significantly reduce the error of student performance prediction, compared to baseline algorithms, while discovering meaningful and surprising relationships among learning resources. [For the full proceedings, see ED593090.]
How Many Friends Can You Make in a Week?: Evolving Social Relationships in MOOCs over Time
Author: Xu, Yiqiao
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Friendship,Online Courses,Peer Relationship,Social Networks,Network Analysis,Academic Achievement,Graduate Students,Data Analysis

Massive Open Online Courses (MOOCs) are designed on the assumption that good students will help poor students thus offloading the individual support tasks from the instructor to the class. However prior research has shown that this is not always true. Students in MOOCs tend to form distinct sub-communities and their grades are closely correlated with those of their closest peers. That work, however, was only based on analyzing the final social network in a MOOC. In this paper, we study the evolution of these co-performing clusters over time. We explore a longitudinal approach to detect how students form their social connections on the discussion forum and we show that students form close coequal communities early in the course and maintain them over the duration of the course. [For the full proceedings, see ED593090.]
Knowledge Tracing Using the Brain
Author: Halpern, David
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Learning Processes,Memory,Prediction,Second Language Learning,Diagnostic Tests,Brain Hemisphere Functions,Language Tests,Models,Accuracy,Neurosciences,Program Descriptions,Markov Processes

Knowledge tracing is a popular and successful approach to modeling student learning. In this paper we investigate whether the addition of neuroimaging observations to a knowledge tracing model enables accurate prediction of memory performance in held-out data. We propose a Hidden Markov Model of memory acquisition related to Bayesian Knowledge Tracing and show how continuous functional magnetic resonance imaging (fMRI) signals can be incorporated as observations related to latent knowledge states. We then show, using data collected from a simple second-language learning experiment, that fMRI data acquired during a learning session can be used to improve predictions about student memory at test. The fitted models can also potentially give new insight into the neural mechanisms that contribute to learning and memory. [For the full proceedings, see ED593090.]
Mining User Trajectories in Electronic Text Books
Author: Boubekki, Ahcène
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Electronic Publishing,Textbooks,Student Behavior,Reader Text Relationship,Proximity

Analyzing user behavior in electronic textbooks offers appealing insights into how pupils interact with the book and internalize the content. Using these insights may help to personalize the book, e.g., to support users with special educational needs. Conventional approaches often focus on atomic, user-triggered events like clicks or scrolls. In this paper, we propose to view all ongoing sessions in a classroom simultaneously and cast the problem as a multi-user problem over space and time. We devise two distance measures to compare the navigation behavior of pupils in different dimensions. Empirically, we observe that our metrics lead to interpretable clusters and serve as performance indicators. [For the full proceedings, see ED593090.]
Differences between M-Learning (Mobile Learning) and E-Learning, Basic Terminology and Usage of M-Learning in Education
Author: Korucu, Agah Tugrul
Date: 2011-00-00
Type: Journal Articles
Subjects: Differences,Electronic Learning,Foreign Countries,Distance Education,Handheld Devices,Computer Uses in Education,Internet,Access to Information,Educational Technology,Telecommunications

The need for usage of technologies which removes the boundaries of time and location increases day by day today when information and accession to information gains importance. Effect of mobile learning to education is an issue to be researched in order to provide lifelong learning. The fact that mobile devices are small and they have got with a lot of features despite their size increases interest for them. This increasing interest requires more study on these devices or causes the usage of these devices in more fields. The feature of mobile devices that enable educational atmosphere encourages individuals for their usage. Besides, it enables an educator who shares the information to contact more students independent of time and location with the usage of mobile devices in education. Mobile learning is explained in a detailed way in this study. Besides, the relation and differences between m-learning and e-learning are put forth with their details. On some important issues such as internet accession and usage status in Turkey, technological devices used in mobile learning and communication technologies are also studied.
STEM Pedagogical Content Knowledge Scale (STEMPCK): A Validity and Reliability Study
Author: Yildirim, Bekir
Date: 2018-00-00
Type: Reports - Research
Subjects: STEM Education,Pedagogical Content Knowledge,Measures (Individuals),Preservice Teachers,Elementary Education,Mathematics Teachers,Science Teachers,Preschool Education,Correlation,Test Reliability,Test Validity,Foreign Countries

The aim of this study was to develop the STEM Pedagogical Content Knowledge Scale (STEMPCK Scale). Exploratory and confirmatory factor analyses were conducted to examine the structural validity of the scale. Exploratory factor analysis of the scale was conducted using data from 443 preservice teachers who were studying to become science teachers, preschool teachers, elementary school teachers, and mathematics teachers. The confirmatory factor analysis of the scale was conducted using data from 212 students who were enrolled in the same departments. The scale was administered to 655 preservice teachers in total. To determine the reliability of the scale, the Cronbach's Alpha internal consistency coefficient, the corrected total item correlation, and the significance of the differences between the item averages of the top 27% and bottom 27% were examined by &quot;t&quot;-test. According to the results obtained, the STEMPCK Scale consists of six factors: 21st-Century Skills, Pedagogical Knowledge, Mathematics, Science, Engineering, and Technology. The scale included 56 items. The Cronbach's Alpha values of the factors ranged from 0.878 to 0.90, and the corrected item total score correlations ranged from 0.306 to 0.895. The results of the &quot;t&quot;-test showed that all the differences between the mean scores of the top 27% and bottom 27% on the items were significant. The results of the analyses indicate that the instrument has reasonable internal consistency and that the theoretical structure was supported by empirical data. These results indicate that the STEMPCK Scale is an appropriate tool for measuring preservice teachers' STEM pedagogical content knowledge. Implications and suggestions for further studies are included. [This report was published in the &quot;Journal of STEM Teacher Education,&quot; v53 n2 Fall 2018.]
Machine Beats Human at Sequencing Visuals for Perceptual-Fluency Practice
Author: Sen, Ayon
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Visual Aids,Visual Perception,Data Analysis,Artificial Intelligence,Sequential Learning,Perceptual Development,Accuracy,Difficulty Level,STEM Education,Instructional Effectiveness

In STEM domains, students are expected to acquire domain knowledge from visual representations that they may not yet be able to interpret. Such learning requires perceptual fluency: the ability to intuitively and rapidly see which concepts visuals show and to translate among multiple visuals. Instructional problems that engage students in nonverbal, implicit learning processes enhance perceptual fluency. Such processes are highly influenced by sequence effects. Thus far, we lack a principled approach for identifying a sequence of perceptual-fluency problems that promote robust learning. Here, we describe a novel educational data mining approach that uses machine learning to generate an optimal sequence of visuals for perceptual-fluency problems. In a human experiment, we show that a machine-generated sequence outperforms both a random sequence and a sequence generated by a human domain expert. Interestingly, the machine-generated sequence resulted in significantly lower accuracy during training, but higher posttest accuracy. This suggests that the machine-generated sequence induced desirable difficulties. To our knowledge, our study is the first to show that an educational data mining approach can induce desirable difficulties for perceptual learning. [For the full proceedings, see ED593090.]
Too Big to Fail: Millennials on the Margins
Author: Sands, Anita
Date: 2018-04-00
Type: Numerical/Quantitative Data
Subjects: Generational Differences,Youth,Late Adolescents,Young Adults,Out of School Youth,Unemployment,Literacy,Numeracy,Reading Skills,Writing Skills,Mathematics Skills,Socioeconomic Status,Gender Differences,Racial Differences,Ethnicity,Language Usage,Social Capital,Individual Characteristics

This report questions how skills are distributed across the millennial population, focusing on the size and demographic characteristics of U.S. millennials with low literacy and numeracy skills, and the resulting impact on social and economic outcomes. The authors do this in part by examining the issue of &quot;disconnected youth,&quot; a term typically applied to those aged 16-24 who are not employed or engaged in formal education or training. In this report, the authors first shift the emphasis of &quot;disconnection&quot; from a concern with attachments to employment and education to one that includes human capital, or skill level. The authors use Programme for the International Assessment of Adult Competencies (PIAAC) skills data to provide a brief international context for the performance of U.S. millennials, followed by an in-depth analysis of the size and demographic dimensions of the problem of low skills in this cohort. The authors argue that low skills are correlated to a larger sense of disconnection and disengagement from political and social life. The author's findings underscore that literacy and numeracy are not only connected with economic returns but play a critical role in uniting our society and democracy.
Predicting Quitting in Students Playing a Learning Game
Author: Karumbaiah, Shamya
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Predictor Variables,Academic Persistence,Educational Games,Play,Learner Engagement,Physics,Science Education,Secondary School Students,Secondary School Science,Grade 8,Grade 9,Prediction,Models

Identifying struggling students in real-time provides a virtual learning environment with an opportunity to intervene meaningfully with supports aimed at improving student learning and engagement. In this paper, we present a detailed analysis of quit prediction modeling in students playing a learning game called Physics Playground. From the interaction log data of the game, we engineered a comprehensive set of aggregated features of varying levels of granularity and trained individualized level-specific models and a single level-agnostic model. Contrary to our initial expectation, our results suggest that a level-agnostic model achieves superior predictive performance. We enhanced this model further with level-related and student-related features, leading to a moderate increase in AUC. Visualizing this model, we observe that it is based on high-level intuitive features that are generalizable across levels. This model can now be used in future work to automatically trigger cognitive and affective supports to motivate students to pursue a game level until completion. [For the full proceedings, see ED593090.]
QuanTyler: Apportioning Credit for Student Forum Participation
Author: Bihani, Ankita
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: College Credits,Classification,Computer Mediated Communication,Student Participation,Predictor Variables,Computer Science Education,College Students,Evaluators,Course Descriptions,Cooperative Learning,Online Courses,Grading,Student Evaluation,Group Discussion

We develop a random forest classifier that helps assign academic credit for a student's class forum participation. The classification target are the four classes created by student rank quartiles. Course content experts provided ground truth by ranking a limited number of post pairs. We expand this labeled set via data augmentation. We compute the relative importance of the predictors, and compare performance in matching the human expert rankings. We reach an accuracy of 0.96 for this task. To test generality and scalability, we trained the classifier on the archive of the Economics Stack Exchange reputation data. We used this classifier to predict the quartile assignments by human judges of forum posts from a university Artificial Intelligence course. Our first attempt at transfer learning reaches an average AUC of 0.66 on the augmented test set. [For the full proceedings, see ED593090.]