ethanporcaro/eric-fulltext-meta huggingface dataset:
Youth Transitions and Network Consolidation. Regional Workshop, 20-22 May 2014 in Gaborone, Botswana. Meeting Report
Author: UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training (Germany)
Date: 2014-00-00
Type: Speeches/Meeting Papers
Subjects: Workshops,Youth Programs,Youth,Employment,Vocational Education,Technical Education,Job Skills,Foreign Countries,International Cooperation,Public Policy,Readiness,Transitional Programs,Skill Development,Barriers

This workshop was designed based on the following objectives: (1) to place youth employment and related issues on the regional political agenda; to highlight regional success stories; (2) to further strengthen the UNEVOC Network consolidation process in the region; (3) to provide a platform for learning and sharing experiences; and (4) to offer an opportunity to actively participate in and shape the regional Technical and Vocational Education and Training (TVET) discourse and to enhance the institutional capacities of the participating UNEVOC Centres. The event was attended by nearly 50 speakers and delegates from eleven countries (Botswana, Ethiopia, Germany, Kenya, Malawi, Mauritius, Mozambique, Republic of Korea, Senegal, South Africa and Zambia). International organizations were represented through UNESCO and the International Labour Organization (ILO), along with the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), Germany's agency for international cooperation. Regional and national TVET institutions (ministries, national TVET bodies, training and research institutions and NGOs) were represented in their capacity as UNEVOC Centres and local institutions. The Programme with the schedule from the meeting and a list of participants are included in the Annex. [This meeting was facilitated by UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training and the Botswana Qualification Authority (BQA).]
Combining Techniques to Refine Item to Skills Q-Matrices with a Partition Tree
Author: Desmarais, Michel C.
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Matrices,Test Items,Skills,Expertise,Methods,Data Analysis,Accuracy

The problem of mapping items to skills is gaining interest with the emergence of recent techniques that can use data for both defining this mapping, and for refining mappings given by experts. We investigate the problem of refining mapping from an expert by combining the output of different techniques. The combination is based on a partition tree that combines the suggested refinements of three known techniques from the literature. Each technique is given as input a Q-matrix, that maps items to skills, and student test outcome data, and outputs a modified Q-matrix that constitutes suggested improvements. We test the accuracy of the partition tree combination techniques over both synthetic and real data. The results over synthetic data show a high improvement over the best single technique with a 86% error reduction on average for four different Q-matrices. For real data, the error reduction is 55%. In addition to the substantial error reduction, the partition tree refinements provide a much more stable performance than any single technique. These results suggest that the partition tree is a valuable refinement combination approach that can effectively take advantage of the complementarity of the Q-matrix refinement techniques. It brings the goal of using a data driven approach to refine the item to skill mapping closer to real applications, although challenges remain and are discussed. [For complete proceedings, see ED560503.]
Seeing the Instructor in Two Video Styles: Preferences and Patterns
Author: Bhat, Suma
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Video Technology,Electronic Learning,Learner Engagement,Student Motivation,Navigation (Information Systems),Preferences,Student Attitudes,Online Courses

Instructional content designers of online learning platforms are concerned about optimal video design guidelines that ensure course effectiveness, while keeping video production time and costs at reasonable levels. In order to address the concern, we use clickstream data from one Coursera course to analyze the engagement, motivational and navigational patterns of learners upon being presented with lecture videos incorporating the instructor video in two styles--first, where the instructor seamlessly interacts with the content and second, where the instructor appears in a window in a portion of the presentation window. Our main empirical finding is that the video style where the instructor seamlessly interacts with the content is by far the most preferred choice of the learners in general and certificate-earners and auditors in particular. Moreover, learners who chose this video style, on average, watched a larger proportion of the lectures, engaged with the lectures for a longer duration and preferred to view the lectures in streamed mode (as opposed to downloading them), when compared to their colleagues who chose the other video style. We posit that the important difference between the two video modes was the integrated view of a &quot;real&quot; instructor in close proximity to the content, that increased learner motivation, which in turn affected the watching times and the proportion of lectures watched. The results lend further credibility to the previously suggested hypothesis that positive affect arising out of improved social cues of the instructor influences learner motivation leading to their increased engagement with the course and its broader applicability to learning at scale scenarios. [For complete proceedings, see ED560503.]
Using Visual Analytics Tool for Improving Data Comprehension
Author: Géryk, Jan
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Animation,Visualization,Visual Stimuli,Program Effectiveness,Statistical Analysis,Higher Education,Data Analysis,Foreign Countries,College Students,Technology Uses in Education,Educational Technology,Hypothesis Testing,Training,Accuracy,Student Attitudes

The efficacy of animated data visualizations in comparison with static data visualizations is still inconclusive. Some researches resulted that the failure to find out the benefits of animations may relate to the way how they are constructed and perceived. In this paper, we present visual analytics (VA) tool which makes use of enhanced animated data visualization methods. The time is an important variable that needs to be modeled in VA. VA methods like Motion Charts show changes over time by presenting animations in two-dimensional space and by changing element appearances. The tool is primarily designed for exploratory analysis of academic analytics and supports various interactive visualization methods which enhance the Motion Charts concept. We evaluate the usefulness and the general applicability of the designed tool with a controlled experiment to assess the efficacy of the described methods. To interpret the experiment results, we utilized one-way repeated measures ANOVA. [For complete proceedings, see ED560503.]
Restructuring Principal Preparation in Illinois: Perspectives on Implementation Successes, Challenges, and Future Outlook. IERC 2015-3
Author: Klostermann, Brenda K.
Date: 2015-00-00
Type: Reports - Research
Subjects: Principals,Administrator Education,Program Implementation,Success,Barriers,Prediction,Mixed Methods Research,Educational Policy,Interviews,Academic Achievement,School Effectiveness,Personnel Selection,Administrator Effectiveness,Elementary Secondary Education

The goals of the current mixed methods study--the Illinois Principal Preparation Implementation Review Project (I-PREP)--are to describe how the new policy is being implemented, learning which aspects of the implementation have been challenging and why they present challenges, and how programs are addressing challenges and realizing improvements in the preparation of their candidates. This report presents findings from the first phase of the study. Results of interviews with program representatives and key Illinois education stakeholders showed the majority of those interviewed support the goals of the new policy and have a positive outlook on its future impact of principal preparation in Illinois. Although some concerns about the implementation were expressed, most believe the redesigned principal preparation programs will ultimately create better prepared school administrators, improved student achievement, and more successful schools. The final report, due in summer 2016, will integrate the findings from this interim report, site visits with selected programs, and a survey with all programs to provide research-based insights in how to improve the policy and its implementation. The following are appended: (1) I-PREP Advisory Board; (2) Study Methodology; and (3) Statewide Scan Interview Protocol. [For the executive summary, see ED560523.]
Restructuring Principal Preparation in Illinois: Perspectives on Implementation Successes, Challenges, and Future Outlook. Executive Summary. IERC 2015-3
Author: Klostermann, Brenda K.
Date: 2015-00-00
Type: Reports - Research
Subjects: Principals,Administrator Education,Program Implementation,Success,Barriers,Prediction,Mixed Methods Research,Educational Policy,Interviews,Academic Achievement,School Effectiveness,Personnel Selection,Administrator Effectiveness,Elementary Secondary Education

The goals of the current mixed methods study--the Illinois Principal Preparation Implementation Review Project (I-PREP)--are to describe how the new policy is being implemented, learning which aspects of the implementation have been challenging and why they present challenges, and how programs are addressing challenges and realizing improvements in the preparation of their candidates. This report presents findings from the first phase of the study. Results of interviews with program representatives and key Illinois education stakeholders showed the majority of those interviewed support the goals of the new policy and have a positive outlook on its future impact of principal preparation in Illinois. Although some concerns about the implementation were expressed, most believe the redesigned principal preparation programs will ultimately create better prepared school administrators, improved student achievement, and more successful schools. The final report, due in summer 2016, will integrate the findings from this interim report, site visits with selected programs, and a survey with all programs to provide research-based insights in how to improve the policy and its implementation. The following are appended: (1) I-PREP Advisory Board; (2) Study Methodology; and (3) Statewide Scan Interview Protocol. [For the full report, see ED560522.]
Mixture Modeling of Individual Learning Curves
Author: Streeter, Matthew
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Probability,Error Patterns,Learning Processes,Models,Knowledge Level,Intelligent Tutoring Systems,Educational Technology,Skill Development,Statistical Analysis,Prediction,Accuracy

We show that student learning can be accurately modeled using a mixture of learning curves, each of which specifies error probability as a function of time. This approach generalizes Knowledge Tracing [7], which can be viewed as a mixture model in which the learning curves are step functions. We show that this generality yields order-of-magnitude improvements in prediction accuracy on real data. Furthermore, examination of the learning curves provides actionable insights into how different segments of the student population are learning. To make our mixture model more expressive, we allow the learning curves to be defined by generalized linear models with arbitrary features. This approach generalizes Additive Factor Models [4] and Performance Factors Analysis [16], and outperforms them on a large, real world dataset. [For complete proceedings, see ED560503.]
Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning
Author: MacLellan, Christopher J.
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Factor Analysis,Regression (Statistics),Knowledge Level,Markov Processes,Bayesian Statistics,Learning Processes,Probability,Statistical Analysis,Models,Performance Factors,Performance Based Assessment,Intelligent Tutoring Systems,Educational Technology

Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions, they differ in their parameterization of student learning. One key difference is that BKT has parameters for the slipping rates of learned skills, whereas the logistic models do not. Thus, the logistic models assume that as students get more practice their probability of correctly answering monotonically converges to 100%, whereas BKT allows monotonic convergence to lower probabilities. In this paper, we present a novel modification of logistic regression that allows it to account for situations resulting in false negative student actions (e.g., slipping on known skills). We apply this new regression approach to create two new methods AFM+Slip and PFA+Slip and compare the performance of these new models to traditional AFM, PFA, and BKT. We find that across five datasets the new slipping models have the highest accuracy on 10-fold cross validation. We also find evidence that the slip parameters better enable the logistic models to fit steep learning rates, rather than better fitting the tail of learning curves as we expected. Lastly, we explore the use of high slip values as an indicator of skills that might benefit from skill label refinement. We find that after refining the skill model for one dataset using this approach the traditional model fit improved to be on par with the slip model. Parameter Estimation is appended. [For complete proceedings, see ED560503.]
Learning Environments and Inquiry Behaviors in Science Inquiry Learning: How Their Interplay Affects the Development of Conceptual Understanding in Physics
Author: Bumbacher, Engin
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Physics,Science Instruction,College Students,Predictor Variables,Comparative Analysis,Inquiry,Learning Activities,Incidence,Task Analysis,Outcomes of Education,Teaching Methods,Computer Simulation,Goal Orientation,Discovery Learning,Science Laboratories,Multivariate Analysis,Science Experiments,Community Colleges,Multiple Regression Analysis,Pretests Posttests,Questionnaires

Studies comparing virtual and physical manipulative environments (VME and PME) in inquiry-based science learning have mostly focused on students' learning outcomes but not on the actual processes they engage in during the learning activities. In this paper, we examined experimentation strategies in an inquiry activity and their relation to conceptual learning outcomes. We assigned college students to either use VME or PME for a goal directed physics inquiry task on mass-spring systems. Our analysis showed that the best predictors of learning outcomes were experimental manipulations that followed a control of variable (CV) strategy, with a delay between manipulations (&quot;systematic inquiry&quot;). Cluster analysis of the prevalence of these manipulations per participant revealed two distinct clusters of participants, systematic inquiry or not. The systematic inquiry cluster had significantly higher learning outcomes than the less systematic one. Furthermore, the majority of the participants using the PME belonged to the more systematic cluster, while most of the participants using the VME fell into the non-systematic cluster, likely because of the specific affordances of the real and virtual equipment they were using. However, beyond this impact on inquiry process, condition had little effect. In light of these results, we argue that investigating processes displayed during learning activities, in addition to outcomes, enables us to properly evaluate the strengths and weaknesses of different learning environments for inquiry-based learning. [For complete proceedings, see ED560503.]
Integrating Learning Styles into Adaptive E-Learning System
Author: Truong, Huong May
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Cognitive Style,Integrated Activities,Electronic Learning,Research Projects,Doctoral Dissertations,Online Courses,Aptitude Treatment Interaction,Research Methodology

This paper provides an overview and update on my PhD research project which focuses on integrating learning styles into adaptive e-learning system. The project, firstly, aims to develop a system to classify students' learning styles through their online learning behaviour. This will be followed by a study on the complex relationship between learning styles, learning supports and learning outcomes. The findings can contribute significantly to the area that is still left with several unanswered questions. In addition, based on the results, meaningful recommendations and suitable online adaptation can also be made to a wide range of stakeholders of the education system. [For complete proceedings, see ED560503.]
Reimagining Khan Analytics for Student Coaches
Author: Cunningham, Jim
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Research Projects,Remedial Mathematics,Coaching (Performance),Visual Aids,Program Effectiveness,Peer Teaching,Progress Monitoring,Data Interpretation,Data Collection,Research Methodology,Intermode Differences,Undergraduate Students

In this paper, I describe preliminary work on a new research project in learning analytics at Arizona State University. In conjunction with an innovative remedial mathematics course using Khan Academy and student coaches, this study seeks to measure the effectiveness of visualized data in assisting student coaches as they help remedial math students achieve success in an online math class. [For complete proceedings, see ED560503.]
Assessing the Roles of Student Engagement and Academic Emotions within Middle School Computer- Based Learning in College-Going Pathways
Author: San Pedro, Maria Ofelia Z.
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Learner Engagement,Psychological Patterns,Middle School Students,Student Behavior,Longitudinal Studies,High School Students,Statistical Analysis,College Bound Students,Outcomes of Education,Student Interests,Educational Experience,Computer Uses in Education,Multiple Regression Analysis,Questionnaires,Multivariate Analysis,Structural Equation Models

This dissertation research focuses on assessing student behavior, academic emotions, and knowledge from a middle school online learning environment, and analyzing their potential effects on decisions about going to college. Using students' longitudinal data ranging from their middle school, to high school, to postsecondary years, I leverage quantitative methodologies to investigate antecedents to college-going outcomes that can occur as early as middle school. The research first looks at whether assessments of learning, emotions and engagement from middle school computer-based curriculum are predictive at all of college-going outcomes years later. I then investigate how these middle school factors can be associated with college-going interests formed in high school, using the same assessments during middle school, together with self-report measures of interests in college when they were in high school. My dissertation then culminates in developing an overall model that examines how student interests in high school can possibly mediate between the educational experiences students have during middle school technology-enhanced learning and their eventual college-going choices. This gives a richer picture of the cognitive and motivational mechanisms that students experience throughout varied phases in their years in school. [For complete proceedings, see ED560503.]
Modeling Speed-Accuracy Tradeoff in Adaptive System for Practicing Estimation
Author: Nižnan, Juraj
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Accuracy,Computation,Models,Item Response Theory,Reaction Time

Estimation is useful in situations where an exact answer is not as important as a quick answer that is good enough. A web-based adaptive system for practicing estimates is currently being developed. We propose a simple model for estimating student's latent skill of estimation. This model combines a continuous measure of correctness and response times. The advantage of the model is its simple update method which makes it directly applicable in the developed adaptive system. [For complete proceedings, see ED560503.]
Modeling Learners' Social Centrality and Performance through Language and Discourse
Author: Dowell, Nia M.
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Learning,Discourse Analysis,Academic Achievement,Social Networks,Network Analysis,Language Styles,Syntax,College Students,Statistical Analysis

There is an emerging trend in higher education for the adoption of massive open online courses (MOOCs). However, despite this interest in learning at scale, there has been limited work investigating the impact MOOCs can play on student learning. In this study, we adopt a novel approach, using language and discourse as a tool to explore its association with two established measures related to learning: traditional academic performance and social centrality. We demonstrate how characteristics of language diagnostically reveal the performance and social position of learners as they interact in a MOOC. We use Coh-Metrix, a theoretically grounded, computational linguistic modeling tool, to explore students' forum postings across five potent discourse dimensions. Using a Social Network Analysis (SNA) methodology, we determine learners' social centrality. Linear mixed-effect modeling is used for all other analyses to control for individual learner and text characteristics. The results indicate that learners performed significantly better when they engaged in more expository style discourse, with surface and deep level cohesive integration, abstract language, and simple syntactic structures. However, measures of social centrality revealed a different picture. Learners garnered a more significant and central position in their social network when they engaged with more narrative style discourse with less overlap between words and ideas, simpler syntactic structures and abstract words. Implications for further research and practice are discussed regarding the misalignment between these two learning-related outcomes. [Additional funding for this research was provided by the Canada Research Chairs Program. For complete proceedings, see ED560503.]
Toward a Real-Time (Day) Dreamcatcher: Sensor-Free Detection of Mind Wandering during Online Reading
Author: Mills, Caitlin
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Reading,Identification,Attention,Reading Rate,Readability,Pacing,Artificial Intelligence,Prediction,Navigation (Information Systems),Internet,Accuracy,Pretests Posttests,Classification,Validity

This paper reports the results from a sensor-free detector of mind wandering during an online reading task. Features consisted of reading behaviors (e.g., reading time) and textual features (e.g., level of difficulty) extracted from self-paced reading log files. Supervised machine learning was applied to two datasets in order to predict if participants were mind wandering as they navigated from one screen of text to the next. Mind wandering was detected with an accuracy of 20% above chance (Cohen's kappa = 0.207; AUC = 0.609), which was obtained via leave-one-participant-out cross-validation. Similar to actual rates of mind wandering, predicted rates of mind wandering were negatively related to posttest performance, thus providing some evidence for the predictive validity of the detector. Applications of the detector to attention-aware educational interfaces are discussed. [For complete proceedings, see ED560503.]