ethanporcaro/eric-fulltext-meta huggingface dataset:
Nursing Educational Programs, 2017 Report
Author: Oakley, Elroy Ortiz
Date: 2017-03-24
Type: Numerical/Quantitative Data
Subjects: Community Colleges,Two Year College Students,Nursing Education,State Aid,Financial Support,Enrollment Trends,Student Attrition,Full Time Equivalency,Academic Persistence,Nursing Students,Educational Finance,Grants,Graduation Rate,Admission Criteria

The California Community Colleges serves more than 2.1 million students each year and is the largest system of higher education in the nation. The state's 113 community colleges are charged with providing workforce training, basic skills education and preparing students to transfer to four-year postsecondary institutions. Seventy-seven California community colleges operate registered nursing programs, enrolling a statewide total of 13,120 full-time equivalent students in 2015-16. Of the 77 colleges with nursing programs, 74 were awarded state funds to expand the enrollment capacity of nursing programs and to implement assessment, remediation and retention strategies to decrease attrition rates. In 2015-16, California community college nursing program enrollment increased by 1,525 students and the overall attrition rate was 13.8 percent. Colleges with nursing programs receive funding through general apportionment by number of full-time equivalent students in each program at each college. This report presents information on categorical funding awarded through an application process to the Chancellor's Office for nursing programs in fiscal year 2015-16. Also included in this report is a summary on Nursing Program Support provided in the Budget Act of 2015, to expand community college nursing enrollments and improve student retention [required by Provision (23) of Item 6870-101-0001 of the Budget Act of 2015].
Nursing Educational Programs Report 2018
Author: Oakley, Elroy Ortiz
Date: 2018-04-27
Type: Reports - Research
Subjects: Community Colleges,Two Year College Students,Nursing Education,State Aid,Financial Support,Enrollment Trends,Student Attrition,Full Time Equivalency,Academic Persistence,Nursing Students,Educational Finance,Grants,Graduation Rate,Admission Criteria

Seventy-seven California community colleges operate registered nursing programs, enrolling a statewide total of 14,010 full-time equivalent students in 2016-17, an increase of 3.6 percent from the previous school year. Of the 77 colleges with nursing programs, 74 were awarded state funds to expand the enrollment capacity of nursing programs and to implement assessment, remediation and retention strategies to decrease attrition rates. In 2016-17, California community college nursing program enrollment increased by 1,514 students (compared to last year's enrollment increase by 1,525 students), and the overall attrition rate was 11.82 percent (compared to last year's 13.8 percent). Colleges with nursing programs receive funding through general apportionment by number of full-time equivalent students in each program at each college. This report presents information on categorical funding beyond this apportionment funding awarded through an application process to the Chancellor's Office for nursing programs in fiscal year 2016-17. Also included in this report is a summary on Nursing Program Support provided in the Budget Act of 2016 to expand community college nursing enrollments and improve student retention [required by Provision (23) of Item 6870-101-0001 of the Budget Act of 2016. For the 2017 report, see ED596527.]
A Latent Factor Model for Instructor Content Preference Analysis
Author: Wang, Jack Z.
Date: 2017-06-00
Type: Reports - Evaluative
Subjects: Item Response Theory,Individualized Instruction,Prediction,Models,Educational Objectives,Classification,Visual Aids,Cognitive Processes,Data,Teachers

Existing personalized learning systems (PLSs) have primarily focused on providing learning analytics using data from learners. In this paper, we extend the capability of current PLSs by incorporating data from instructors. We propose a latent factor model that analyzes instructors' preferences in explicitly &quot;excluding&quot; particular questions from learners' assignments in a particular subject domain. We formulate the problem of predicting instructors' question exclusion preferences as a matrix factorization problem, and incorporate expert-labeled Bloom's Taxonomy tags on each question as a factor in our statistical model to improve model interpretability. Experimental results on a real-world educational dataset demonstrate that the proposed model achieves superior prediction performance compared to several other baseline methods commonly used in recommender systems. Additionally, by explicitly incorporating Bloom's Taxonomy, the model provides meaningful interpretations that help understand why instructors exclude certain questions. Since instructor preference data contains their insights after years of teaching experience, our proposed model has the potential to further improve the question recommendations that PLSs make for learners. [For the full proceedings, see ED596512.]
Evaluation of a Data-Driven Feedback Algorithm for Open-Ended Programming
Author: Price, Thomas
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Feedback (Response),Computer Software,Data Analysis,Programming,Computer Science Education,Comparative Analysis,Intelligent Tutoring Systems,Teacher Student Relationship,Teaching Methods,Help Seeking,Introductory Courses,Undergraduate Students,Course Descriptions,Research Universities,Prediction,Mathematics

In this paper we present a novel, data-driven algorithm for generating feedback for students on open-ended programming problems. The feedback goes beyond next-step hints, annotating a student's whole program with suggested edits, including code that should be moved or reordered. We also build on existing work to design a methodology for evaluating this feedback in comparison to human tutor feedback, using a dataset of real student help requests. Our results suggest that our algorithm is capable of reproducing ideal human tutor edits almost as frequently as another human tutor. However, our algorithm also suggests many edits that are not supported by human tutors, indicating the need for better feedback selection. [For the full proceedings, see ED596512.]
Generalizability of Face-Based Mind Wandering Detection across Task Contexts
Author: Stewart, Angela
Date: 2017-06-00
Type: Reports - Evaluative
Subjects: Attention,Reading,Films,Nonverbal Communication,Human Body,Motion,Models,Generalization,Undergraduate Students,Reading Comprehension,Artificial Intelligence

We investigate generalizability of face-based detectors of mind wandering across task contexts. We leveraged data from two lab studies: one where 152 college students read a scientific text and another where 109 college students watched a narrative film. We automatically extracted facial expressions and body motion features, which were used to train supervised machine learning models on each dataset, as well as a concatenated dataset. We applied models from each task context (scientific text or narrative film) to the alternate context to study generalizability. We found that models trained on the narrative film dataset generalized to the scientific text dataset with no modifications, but the predicted mind wandering rate needed to be adjusted before models trained on the scientific text dataset would generalize to the narrative film dataset. Additionally, we analyzed generalizability of individual features and found that the lip tightener and jaw drop action units had the greatest potential to generalize across task contexts. We discuss findings and applications of our work to attention-aware learning technologies. [For the full proceedings, see ED596512.]
Addressing Student Behavior and Affect with Empathy and Growth Mindset
Author: Karumbaiah, Shamya
Date: 2017-06-00
Type: Reports - Research
Subjects: Affective Behavior,Empathy,Correlation,Mathematics Skills,Self Concept,Student Attitudes,Intelligent Tutoring Systems,Feedback (Response),Mathematics Instruction,Grade 6

We present results of a randomized controlled study that compared different types of affective messages delivered by pedagogical agents. We used animated characters that were empathic and emphasized the malleability of intelligence and the importance of effort. Results showed significant correlations between students who received more &quot;empathic messages&quot; and those who were &quot;more confident,&quot; &quot;more patient,&quot; exhibited &quot;higher&quot; levels of &quot;interest,&quot; and &quot;valued&quot; math knowledge more. Students who received more growth mindset messages, tended to get more problems correct on their first attempt but valued math knowledge less and had lower &quot;posttest scores.&quot; Students who received more &quot;success/failure&quot; messages tended to make &quot;more mistakes,&quot; to be &quot;less learning-oriented,&quot; and stated that they were &quot;more confused.&quot; We conclude that these affective messages are powerful media to influence students' perceptions of themselves as learners, as well as their perceptions of the domain being taught. We have reported significant results that support the use of empathy to improve student affect and attitudes in a math tutor. [For the full proceedings, see ED596512.]
Adaptive Sequential Recommendation for Discussion Forums on MOOCs Using Context Trees
Author: Mi, Fei
Date: 2017-06-00
Type: Reports - Evaluative
Subjects: Online Courses,Sequential Approach,Discussion Groups,Student Interests,Preferences,Users (Information),Pattern Recognition,Markov Processes,Prediction,Intelligent Tutoring Systems

Massive open online courses (MOOCs) have demonstrated growing popularity and rapid development in recent years. Discussion forums have become crucial components for students and instructors to widely exchange ideas and propagate knowledge. It is important to recommend helpful information from forums to students for the benefit of the learning process. However, students or instructors update discussion forums very often, and the student preferences over forum contents shift rapidly as a MOOC progresses. So, MOOC forum recommendations need to be adaptive to these evolving forum contents and drifting student interests. These frequent changes pose a challenge to most standard recommendation methods as they have difficulty adapting to new and drifting observations. We formalize the discussion forum recommendation problem as a sequence prediction problem. Then we compare different methods, including a new method called context tree (CT), which can be effectively applied to online sequential recommendation tasks. The results show that the CT recommender performs better than other methods for MOOCs forum recommendation task. We analyze the reasons for this and demonstrate that it is because of better adaptation to changes in the domain. This highlights the importance of considering the adaptation aspect when building recommender system with drifting preferences, as well as using machine learning in general. [For the full proceedings, see ED596512.]
An Effective Framework for Automatically Generating and Ranking Topics in MOOC Videos
Author: Zhu, Jile
Date: 2017-06-00
Type: Reports - Research
Subjects: Large Group Instruction,Online Courses,Educational Technology,Technology Uses in Education,Video Technology,Data Collection,Data Analysis,Lecture Method,Scaling,Feedback (Response)

Although millions of students have access to varieties of learning resources on Massive Open Online Courses (MOOCs), they are usually limited to receiving rapid feedback. Providing guidance for students, which enhances the interaction with students, is a promising way to improve learning experience. In this paper, we consider to show students the emphasis of lectures before their learning. We propose a novel framework that automatically generates and ranks the topics within the upcoming chapter. We apply the Latent Dirichlet Allocation (LDA) model on the subtitles of lectures to generate topics. We then rank the importance of these topics through a particular PageRank method, which also leverages structural information of lectures. Experimental results demonstrate the effectiveness of our approach, with a 18.9% improvement in Mean Average Precision (MAP). At last, we simulate two cases to discuss how can our framework guide students according to their learning status. [For the full proceedings, see ED596512.]
An Extended Learner Modeling Method to Assess Students&apos; Learning Behaviors
Author: Dong, Yi
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Student Behavior,Models,Monte Carlo Methods,Learning Processes,Middle School Students,Grade 6,Computer Software,Educational Technology,Technology Uses in Education,Scaffolding (Teaching Technique),STEM Education

This paper discusses a novel approach for developing more refined and accurate learner models from student data collected from Open Ended Learning Environments (OELEs). OELEs provide students choice in how they go about constructing solutions to problems, and students exhibit a variety of learning behaviors in such environments. Building accurate models from limited amount of student data is difficult; to address this we develop a methodology that uses Monte Carlo Tree Search methods to boost the initial set of student action sequences in such a way that we can learn more accurate models of students' learning behaviors. We use an HMM representation to model students' learning behaviors and demonstrate the effectiveness of our approach by running a case study on data collected from 98 students, who worked with the Betty's Brain system for four days. The results have interesting implications for learner modeling and its applications to adaptive scaffolding of students' learning behaviors and strategies as they learn from OELEs. [For the full proceedings, see ED596512.]
Gaze-Based Detection of Mind Wandering during Lecture Viewing
Author: Hutt, Stephen
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Eye Movements,Attention,Lecture Method,Student Behavior,Online Courses,Large Group Instruction,Models,Intelligent Tutoring Systems,Video Technology,Foreign Countries,Undergraduate Students

We investigate the use of consumer-grade eye tracking to automatically detect Mind Wandering (MW) during learning from a recorded lecture, a key component of many Massive Open Online Courses (MOOCs). We considered two feature sets: stimulus-independent global gaze features (e.g., number of fixations, fixation duration), and stimulus-dependent local features. We trained Bayesian networks using the aforementioned features and students? self-reports of MW and validated them in a manner that generalized to new students. Our results indicated that models built with global features (F[subscript 1] MW = 0.47) outperformed those using local features (F[subscript 1] MW = 0.34) and a chance-level model (F[subscript 1] MW = 0.30). We discuss our results in the context of MOOC development as well as integrating MW detection into attention-aware MOOCs. [For the full proceedings, see ED596512.]
Assessing Computer Literacy of Adults with Low Literacy Skills
Author: Olney, Andrew M.
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Computer Literacy,Reading Comprehension,Intelligent Tutoring Systems,Correlation,Adult Learning,Adult Literacy,Computer Assisted Instruction,Reading Instruction,Prediction,Task Analysis,Item Analysis,Keyboarding (Data Entry),Tests

Adaptive learning technologies hold great promise for improving the reading skills of adults with low literacy, but adults with low literacy skills typically have low computer literacy skills. In order to determine whether adults with low literacy skills would be able to use an intelligent tutoring system for reading comprehension, we adapted a 44 task computer literacy assessment and delivered it to 114 adults with reading skills between 3rd and 8th grade levels. This paper presents four analyses on these data. First, we report the pass/fail data natively exported by the assessment for particular computer-based tasks. Second, we undertook a GOMS analysis of each computer-based task, to predict the task completion time for a skilled user, and found that it negatively correlated with proportion correct for each item, r(42) = -0.4, p = 0.01. Third, we used the GOMS task decomposition to develop a Q-matrix of component computer skills for each task, and using logistic mixed effects models on this matrix identified five component skills highly predictive of the success or failure of an individual on a computer task: function keys, typing, using icons, right clicking, and mouse dragging. And finally, we assessed the predictive value of all component skills using logistic lasso. [For the full proceedings, see ED596512. For related grantee submission, see ED596350.]
Assessing the Dialogic Properties of Classroom Discourse: Proportion Models for Imbalanced Classes
Author: Olney, Andrew M.
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Models,Classroom Communication,Audio Equipment,Discourse Analysis,Classification,Correlation,Speech Communication,Classroom Observation Techniques,Teaching Methods,Instructional Effectiveness,Educational Quality,Teacher Effectiveness,Classroom Environment,Validity,Computer Software,Data Analysis

Automatic assessment of dialogic properties of classroom discourse would benefit several widespread classroom observation protocols. However, in classrooms with low incidences of dialogic discourse, assessment can be highly biased against detecting dialogic properties. In this paper, we present an approach to addressing this imbalanced class problem. Rather than perform classifications at the utterance level, we aggregate feature vectors to classify proportions of dialogic properties at the class-session level and achieve a moderate correlation with actual proportions, r(130) = 0.50, p &lt; 0.001, CI[subscript 95][0.36, 0.61]. We show that this approach outperforms aggregating utterance level classifications, r(130) = 0.27, p = 0.001, CI[subscript 95][0.11, 0.43], is stable for both low and high dialogic classrooms, and is stable across both automatic speech recognition and human transcripts. [For the full proceedings, see ED596512.]
Grade Prediction with Temporal Course-Wise Influence
Author: Ren, Zhiyun
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Grade Prediction,Time Perspective,Educational Technology,Time to Degree,Undergraduate Students,Student Records,Academic Degrees

There is a critical need to develop new educational technology applications that analyze the data collected by universities to ensure that students graduate in a timely fashion (4 to 6 years); and they are well prepared for jobs in their respective fields of study. In this paper, we present a novel approach for analyzing historical educational records from a large, public university to perform next-term grade prediction; i.e., to estimate the grades that a student will get in a course that he/she will enroll in the next term. Accurate next-term grade prediction holds the promise for better student degree planning, personalized advising and automated interventions to ensure that students stay on track in their chosen degree program and graduate on time. We present a factorization-based approach called Matrix Factorization with Temporal Course-wise Influence that incorporates course-wise influence effects and temporal effects for grade prediction. In this model, students and courses are represented in a latent &quot;knowledge&quot; space. The grade of a student on a course is modeled as the similarity of their latent representation in the &quot;knowledge&quot; space. Course-wise influence is considered as an additional factor in the grade prediction. Our experimental results show that the proposed method outperforms several baseline approaches and infer meaningful patterns between pairs of courses within academic programs. [For the full proceedings, see ED596512.]
Development of a Trajectory Model for Visualizing Teacher ICT Usage Based on Event Segmentation Data
Author: Zheng, Longwei
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Educational Technology,Technology Uses in Education,Visualization,Data Collection,Data Analysis,Teaching Methods,Electronic Publishing,Textbooks,Elementary School Teachers,Elementary School Students,Foreign Countries

The adoption of educational technologies such as e-textbook has offered a new opportunity to gain insight into teachers' usage of ICT (Information and Communication Technologies). In the e-textbook platform, customized digital products and the learning activities organized in digital environment require teachers to make greater efforts in planning lessons and producing resources. In addition, usage of technology can vary greatly from one group of teachers from another in various contexts. In this study, we demonstrate how computations like event segmentation and contextual numbers can be exploited in visualizing trajectories of teacher's ICT usage. We also study with the experience structure via the implicit patterns within the raw data of an e-textbook platform. Such automated visual characterization might be helpful to the wide and scalable application of teaching analytics to represent teacher's ICT usage. [For the full proceedings, see ED596512.]
Automated Assessment for Scientific Explanations in On-Line Science Inquiry
Author: Li, Haiying
Date: 2017-06-00
Type: Speeches/Meeting Papers
Subjects: Automation,Science Instruction,Inquiry,Educational Assessment,Intelligent Tutoring Systems,Online Courses,Scientific Concepts,Thinking Skills,Evidence,Scoring Rubrics,Middle School Students,Test Reliability

Scientific explanations, which include a claim, evidence, and reasoning (CER), are frequently used to measure students' deep conceptual understandings of science. In this study, we developed an automated scoring approach for the CER that students constructed as a part of virtual inquiry (e.g., formulating questions, analyzing data, and warranting claims) in an intelligent tutoring system (ITS), called Inq-ITS. Results showed that the automated scoring of CER was strongly correlated with human scores when validated using independent sets of data from both the same inquiry task/question, as well as when using data from a different inquiry task/question. These findings imply that automated CER is a very promising approach to reliably and efficiently score scientific explanations in open response format for both small- and largescale assessments. It also provides Inq-ITS with the capability to assess the full complement of inquiry practices described by NGSS. [For the full proceedings, see ED596512.]
Analysis of Problem-Solving Behavior in Open-Ended Scientific-Discovery Game Challenges
Author: Bauer, Aaron
Date: 2017-06-00
Type: Reports - Research
Subjects: Problem Solving,Science Instruction,Cooperative Learning,Visualization,Scientific Principles,Discovery Processes,Educational Games,Puzzles,Teaching Methods,Student Behavior

Problem-solving skills in creative, open-ended domains are both important and little understood. These domains are generally ill-structured, have extremely large exploration spaces, and require high levels of specialized skill in order to produce quality solutions. We investigate problem-solving behavior in one such domain, the scientific-discovery game &quot;Foldit&quot;. Our goal is to discover differentiating patterns and understand what distinguishes high and low levels of problem-solving skill. To address the challenges posed by the scale, complexity, and ill-structuredness of &quot;Foldit&quot; solver behavior data, we devise an iterative visualization-based methodology and use this methodology to design a concise, meaning-rich visualization of the problem-solving process in &quot;Foldit&quot;. We use this visualization to identify key patterns in problem-solving approaches, and report how these patterns distinguish high-performing solvers in this domain. [For the full proceedings, see ED596512.]