ethanporcaro/eric-fulltext-meta huggingface dataset:
Expediting Support for Social Learning with Behavior Modeling
Author: Jo, Yohan
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Information Retrieval,Learning Processes,Interaction,Interpersonal Relationship,Behavior Modification,Modeling (Psychology),Intervention,Goal Orientation,Role Models,Academic Persistence,Learner Engagement,Large Group Instruction,Online Courses,Educational Technology,Technology Uses in Education,Social Networks,Network Analysis,Educational Research,Data Collection,Data Analysis

An important research problem for Educational Data Mining is to expedite the cycle of data leading to the analysis of student learning processes and the improvement of support for those processes. For this goal in the context of social interaction in learning, we propose a three-part pipeline that includes data infrastructure, learning process analysis with behavior modeling, and intervention for support. We also describe an application of the pipeline to data from a social learning platform to investigate appropriate goal-setting behavior as a qualification of role models. Students following appropriate goal setters persisted longer in the course, showed increased engagement in hands-on course activities, and were more likely to review previously covered materials as they continued through the course. To foster this beneficial social interaction among students, we propose a social recommender system and show potential for assisting students in interacting with qualified goal setters as role models. We discuss how this generalizable pipeline can be adapted for other support needs in online learning settings. [For the full proceedings, see ED592609.]
The Contribution of Neuroimaging to the Study of Reading Comprehension
Author: Perkins, Kyle
Date: 2019-02-12
Type: Reports - Evaluative
Subjects: Reading Comprehension,Neurolinguistics,Diagnostic Tests,Brain Hemisphere Functions,Reading Difficulties,Teaching Methods,Familiarity,Reading Instruction,Cognitive Ability,Grammar

In this position paper, we advocate that advancements made in other disciplinary areas such as neurolinguistics should be included into contemporary reading comprehension courses and programs. We present findings from neurobiology of reading that suggest explanation of certain reading behaviors: (1) the differences between reading disability and typically developing readers; (2) an inverted U-shaped function that reflects the fact that learning to read is associated with increased activation (the rising part of the inverted U) and activation decreases are associated with familiarity, experience, and expertise (the falling part of the inverted U); (3) and, the identification of reading networks. As potential pedagogical implications of neuroimaging studies to reading, a list of sentence structures is proposed as an example to further relate reading comprehension to cognitive capacity limits.
Exploring Learning Management System Interaction Data: Combining Data-Driven and Theory-Driven Approaches
Author: Choi, Hongkyu
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Integrated Learning Systems,Data Analysis,Multivariate Analysis,Multiple Regression Analysis,Classification,Undergraduate Students,Prediction,Grades (Scholastic),Academic Persistence

This research connects several data-driven educational data mining approaches to a framework for interaction developed in educational research. In particular, 10 million usage data points collected by a Learning Management System used by students and teachers in 450 online undergraduate courses were analyzed with this framework. A range of educational data mining techniques were employed, including K-means clustering, multiple regression, and classification, to both explore and predict student final grades and course completion rates. Findings show that support for the overall model varied with the way data were mapped to the framework (e.g., static vs. temporal features) and the analysis technique used (with clustering and classification providing more useful insights). [For the full proceedings, see ED592609.]
Language Immersion Programs for Dominican College Students/From the Russian Experience to the MESCyT Experience.
Author: Tavarez DaCosta, Pedro
Date: 2019-00-00
Type: Reports - Evaluative
Subjects: Foreign Countries,Second Language Learning,Second Language Instruction,Russian,English (Second Language),Immersion Programs,College Students,Comparative Education,Applied Linguistics,Language Acquisition,Teaching Methods,Learning Theories,Language Proficiency,Bilingualism,Difficulty Level

The following work consists of a comparative research of two of the most outstanding immersion programs for foreign languages (Russian/English) that Dominican college students have ever undergone. The first one, happened decades ago in the cold distant Union of Soviet Socialist Republic where thousands of our students were sent as scholarship's recipient to study in different Russian universities, after being selected by the Dominican Communist Party, and sponsored by the Soviet Government. The second one, is still happening in our country with the offering of the so-called English Immersion Program sponsored since 2005 by the Ministry of Higher Education-MESCyT. The aim of this study it is to place emphasis on the Total immersion Program on the Russian language they had to face, before being enrolled in their professional schools, as a logical and natural requirement for achieving their goals on the selected career, at the selected university. The data collected through interviews and a survey conducted to a meaningful bunch of those former scholarship's recipient, as well as, analyzing the data published at hand, pretty well served us as the necessary elements for comparing and analyzing the so-called English Immersion program. which is currently sponsored by the Ministry of Higher Education of our country, with that of the Russian program. An extensive literary review has also been gathered in order to determine and clarify how some immersion programs have functioned in many others countries, and the methodological criteria that launched them. To that effect many circumstances not only on the exclusivity of the field of Applied Linguistics, Language Acquisition Theories and EFL Teaching Methodology have to be weighed on, to scientifically examine the program in our country but also circumstances attaining the socio-political phenomena that prevails in our country, as a direct influence on education in general.
{ENTER}ing the Time Series {SPACE}: Uncovering the Writing Process through Keystroke Analyses
Author: Allen, Laura K.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Writing Processes,Intelligent Tutoring Systems,Natural Language Processing,Feedback (Response),Keyboarding (Data Entry),Prompting,Essays,Undergraduate Students,Scoring,Writing (Composition),Writing Skills,Writing Evaluation

This study investigates how and whether information about students' writing can be recovered from basic behavioral data extracted during their sessions in an intelligent tutoring system for writing. We calculate basic and time-sensitive keystroke indices based on log files of keys pressed during students' writing sessions. A corpus of prompt-based essays was collected from 126 undergraduates along with keystrokes logged during the session. Holistic scores and linguistic properties of these essays were then automatically calculated using natural language processing tools. Results indicated that keystroke indices accounted for 76% of the variance in essay quality and up to 38% of the variance in the linguistic characteristics. Overall, these results suggest that keystroke analyses can help to recover crucial information about writing, which may ultimately help to improve student models in computer-based learning environments. [For the corresponding grantee submission, see ED586518. For the full proceedings, see ED592609.]
Creativity as a Key Component in Different National Settings for EFL Teaching
Author: Tavarez DaCosta, Pedro
Date: 2019-00-00
Type: Reports - Research
Subjects: English (Second Language),Second Language Learning,Second Language Instruction,Creative Teaching,Creativity,Language Teachers,Learning Theories,Case Studies,Foreign Countries,Cross Cultural Studies,Outcomes of Education

The creativity that teachers and professors as well, exercise in a classroom, is an immeasurable potential for exploiting their student's opportunities and abilities to fully develop their language skills, as well as, their character and personality; it is the kind of driving force that impulse our pupils beyond their current capacities, and catapult them into new, higher and marvelous horizons. This exploratory research examines how creativity enhances the processes of learning and acquiring EFL in different national settings, where it was compared the different degrees of success attained by teachers and students as well, as a clear indicator of good practices in the classroom and in education in general. For doing so, we first analyzed on the lights of the great theories on the matter, that is a great discussion on the literature available was held, and on the light of the data gathered by scholars of different countries, and nations so distant that clearly defines them as antipodes, the whole picture was in that way completed. Despite the fact that this exploratory research completely focused on foreign language learning/acquisition, the findings and conclusions herein discussed applies to any field of education regardless the area and regardless the level. It is really hoped through this work, that the knowledge extracted from this pedagogical experience could serve as a guiding line for other nations that share common characteristics of the country of our region, and therefore, the experience could be assumed and assimilated by others as well.
Exploring the Impact of Data-Driven Tutoring Methods on Students&apos; Demonstrative Knowledge in Logic Problem Solving
Author: Mostafav, Behrooz
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Data,Intelligent Tutoring Systems,Problem Solving,Mathematical Logic,Validity,Undergraduate Students,Instructional Effectiveness,Mathematics Instruction

We have been incrementally adding data-driven methods into the Deep Thought logic tutor for the purpose of creating a fully data-driven intelligent tutoring system. Our previous research has shown that the addition of data-driven hints, worked examples, and problem assignment can improve student performance and retention in the tutor. In this study, we investigate how the addition of these methods affects students' demonstrative knowledge of logic proof solving using their post-tutor examination scores. We have used data collected from three test conditions with different combinations of our data-driven additions to determine which methods are most beneficial to students who demonstrate higher or lower knowledge of the subject matter. Our results show that students who are assigned problems based on profiling proficiency compared to prior exemplary students with similar problem-solving behavior show higher examination scores overall, and the use of proficiency profiling increases retention and reduces the amount of time taken in-tutor for lower performing students in particular. The results from this study also helps differentiate the behavior of higher and lower performing students in tutor, which can allow quicker interventions for lower proficiency students. [For the full proceedings, see ED592609.]
Closing the Loop with Quantitative Cognitive Task Analysis
Author: Koedinger, Kenneth R.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Educational Research,Data Collection,Task Analysis,Cognitive Processes,Models,Matrices,Middle School Students,Secondary School Mathematics,Algebra,Instructional Design,Mathematics Instruction,Mathematics Skills,Problem Solving,Difficulty Level

Many educational data mining studies have explored methods for discovering cognitive models and have emphasized improving prediction accuracy. Too few studies have &quot;closed the loop&quot; by applying discovered models toward improving instruction and testing whether proposed improvements achieve higher student outcomes. We claim that such application is more effective when interpretable, explanatory models are produced. One class of such models involves a matrix mapping hypothesized (and typically human labeled) latent knowledge components (KCs) to the instructional practice tasks that require them. An under-investigated assumption in these models is that both task difficulty and learning transfer are modeled and predicted by the same latent KCs. We provide evidence for this assumption. More specifically, we investigate the data-driven hypothesis that competence with Algebra story problems may be better enhanced not through story problem practice but through, apparently task irrelevant, practice with symbolic expressions. We present new data and analytics that extend a prior close-the-loop study to 711 middle school math students. The results provide evidence that &quot;quantitative cognitive task analysis&quot; can use data from task difficulty differences to aid discovery of cognitive models that include non-obvious or hidden skills. In turn, student learning and transfer can be improved by closing the loop through instructional design of novel tasks to practice those hidden skills. [For the full proceedings, see ED592609.]
Going Deeper with Deep Knowledge Tracing
Author: Xiong, Xiaolu
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Computer Software,Bayesian Statistics,Knowledge Level,Difficulty Level,Mastery Learning,Scaffolding (Teaching Technique),Sequential Approach,Learning Processes,Data Analysis,Performance Based Assessment,Student Evaluation,Prediction,Comparative Analysis

Over the last couple of decades, there have been a large variety of approaches towards modeling student knowledge within intelligent tutoring systems. With the booming development of deep learning and large-scale artificial neural networks, there have been empirical successes in a number of machine learning and data mining applications, including student knowledge modeling. Deep Knowledge Tracing (DKT), a pioneer algorithm that utilizes recurrent neural networks to model student learning, reports substantial improvements in prediction performance. To help the EDM community better understand the promising techniques of deep learning, we examine DKT alongside two well-studied models for knowledge modeling, PFA and BKT. In addition to sharing a primer on the internal computational structures of DKT, we also report on potential issues that arise from data formatting. We take steps to reproduce the experiments of Deep Knowledge Tracing by implementing a DKT algorithm using Google's TensorFlow framework; we also reproduce similar results on new datasets. We determine that the DKT findings do not hold an overall edge when compared to the PFA model, when applied to properly prepared datasets that are limited to main (i.e. non-scaffolding) questions. More importantly, during the investigation of DKT, we not only discovered a data quality issue in a public available data set, but we also detected a vulnerability of DKT at how it handles multiple skill sequences. [Additional funding for this paper was provided by the U.S. Department of Education's Graduate Assistance in Areas of National Need (GAANN). For the full proceedings, see ED592609.]
Course Content Analysis: An Initiative Step toward Learning Object Recommendation Systems for MOOC Learners
Author: Dai, Yiling
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Large Group Instruction,Online Courses,Course Content,Educational Technology,Technology Uses in Education,Resource Units,Cues,Individualized Instruction,Content Analysis,Course Descriptions

With the accelerating development of open education, low-cost online learning resources, such as Massive Open Online Courses (MOOCs), are reaching a wide audience around the world. However, when faced with these appealing but overwhelming learning resources, learners are prone making rash learning decisions, which may be either excessive or insufficient to their learning capacities. To avoid the mismatch between learners and learning objects, we propose a supporting system that recommends a personalized path of learning objects for a given learner. In realizing this system, a domain knowledge structure is necessary to connect learners' information and learning objects. As an initiative step, we employ the Labeled Latent Dirichlet Allocation method to predict how the content of a course is distributed over different categories in the domain. We conduct experiments by utilizing course syllabi as course content, and curriculum guidelines as domain knowledge. The predicting performance is improved when involving external texts related to the concerned domain knowledge unit. [For the full proceedings, see ED592609.]
Course Enrollment Recommender System
Author: Bydžovská, Hana
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Enrollment,College Students,Course Selection (Students),Data Analysis,Foreign Countries,Computer Uses in Education,Courses

One of the main problems faced by university students is to create and manage the semester course plan. In this paper, we present a course enrollment recommender system based on data mining techniques. The system mainly helps with students' enrollment decisions. More specifically, it provides recommendation of selective and optional courses with respect to students' skills, knowledge, interests and free time slots in their timetables. The system also warns students against difficult courses and reminds them mandatory study duties. We evaluate the usability of designed methods by analyzing real-world data obtained from the Information System of Masaryk University.[For the full proceedings, see ED592609.]
Generating Data-Driven Hints for Open-Ended Programming
Author: Price, Thomas W.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Programming,Data,Computer Science Education,Undergraduate Students,Instructional Effectiveness,Mathematics,Feedback (Response)

Intelligent Tutoring Systems (ITSs) have shown success in the domain of programming, in part by providing customized hints and feedback to students. However, many popular novice programming environments still lack these intelligent features. This is due in part to their use of open-ended programming assignments, which are difficult to support with existing hint generation techniques. In this paper, we present a new data-driven algorithm, based on the Hint Factory, to generate hints for these open-ended assignments. We evaluate our algorithm on historical student data and show that it can provide hints that successfully lead students to solutions from any state, help students achieve assignment objectives, and align with the student's future solution. [For the full proceedings, see ED592609.]
Data-Driven Automated Induction of Prerequisite Structure Graphs
Author: Chaplot, Devendra Singh
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Graphs,Automation,Instructional Materials,Data,College Students,Online Courses,Information Utilization

With the growing popularity of MOOCs and sharp trend of digitalizing education, there is a huge amount of free digital educational material on the web along with the activity logs of large number of participating students. However, this data is largely unstructured and there is hardly any information about the relationship between material from different sources. We propose a generic algorithm to use educational material and student activity data from heterogeneous sources to create a Prerequisite Structure Graph (PSG). A PSG is a directed acyclic graph, where the nodes are educational units and the edges specify the pairwise ordering of the units in effective teaching by instructors or for effective learning by students. We propose an unsupervised approach utilizing both text content and student data, which outperforms to supervised methods (utilizing only text content) on the task of estimating a PSG. [For the full proceedings, see ED592609.]
Joint Discovery of Skill Prerequisite Graphs and Student Models
Author: Chen, Yetian
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Bayesian Statistics,Prerequisites,Graphs,Intelligent Tutoring Systems,Student Evaluation,Models,Accuracy

Skill prerequisite information is useful for tutoring systems that assess student knowledge or that provide remediation. These systems often encode prerequisites as graphs designed by subject matter experts in a costly and time-consuming process. In this paper, we introduce &quot;Combined student Modeling and prerequisite Discovery&quot; (COMMAND), a novel algorithm for jointly inferring a prerequisite graph and a student model from data. Learning a COMMAND model requires student performance data and a mapping of items to skills (Q-matrix). COMMAND learns the skill prerequisite relations as a Bayesian network (an encoding of the probabilistic dependence among the skills) via a two-stage learning process. In the first stage, it uses an algorithm called Structural Expectation Maximization to select a class of equivalent Bayesian networks; in the second stage, it uses curriculum information to select a single Bayesian network. Our experiments on simulations and real student data suggest that COMMAND is better than prior methods in the literature. [For the full proceedings, see ED592609.]
Investigating Swarm Intelligence for Performance Prediction
Author: Majid al-Rifaie, Mohammad
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Student Behavior,Online Courses,Social Behavior,Data Analysis,Socialization,Large Group Instruction,Educational Technology,Interaction,Learning Activities,Probability,Programming

This paper proposes a new technique for analysing the behaviour of students on an online course. This work considers a range of social learning behaviours supported in our recently designed and implemented collaborative learning system which supports students giving and receiving feedback on each other's developing work and practice. The course was delivered to several thousand students on Coursera during which students were directed onto our social learning environment to take part in group work and assessment activities. This work introduces a swarm intelligence technique, Stochastic Diffusion Search (SDS), and shows how it can be adapted and applied to our data in order to perform classification tasks. The novelty of the approach is not only in using this technique, but also applying it to data linked to &quot;social behaviour&quot; (i.e. how students interact with each other) which differentiates the work apart from many clickstream analysis studies. This paper investigates what combined activity is the best predictor of success or failure in the course. The aim is to argue that the results obtained using the proposed approach indicate the promising potential of predicting students performance through applying swarm intelligence technique to social behaviours. This work has a number of potential benefits including designing better social learning systems, designing more effective social learning and assessment exercises, and encouraging disengaged students. In addition, this work is an important step in addressing our long term goal of evidencing how critical student learning takes place as they give and receive feedback to and from each other on work in progress. [For the full proceedings, see ED592609.]