ethanporcaro/eric-fulltext-meta huggingface dataset:
Beyond Log Files: Using Multi-Modal Data Streams towards Data-Driven KC Model Improvement
Author: Liu, Ran
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Educational Technology,Data Analysis,Automation,Data,Models,Improvement,Knowledge Representation,High School Students,Chemistry,Intelligent Tutoring Systems,Middle School Students,Fractions,Grade 5

The increasing use of educational technologies in classrooms is producing vast amounts of process data that capture rich information about learning as it unfolds. The field of educational data mining has made great progress in using log data to build models that improve instruction and advance the science of learning. Thus far, however, the predictive and explanatory power of such models has often been limited to the actions that educational technologies can log. A major challenge in incorporating more contextually rich data streams into models of learning is collecting and integrating data from different sources and at different grain sizes. We present our methodological advances in automating the integration of log data with additional multi-modal (e.g., audio, screen video, webcam video) data streams. We also demonstrate several examples of how integrating multiple streams of data into the knowledge component (KC) model refinement process improves the predictive fit of student models and yields important pedagogical implications. This work represents an important advancement in facilitating the integration of rich qualitative details of students' learning contexts into the quantitative approaches characteristic of EDM research. [For the full proceedings, see ED592609.]
Calibrated Self-Assessment
Author: Labutov, Igor
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Self Evaluation (Individuals),Student Evaluation,Item Response Theory,Grading,Incentives

Peer-grading is widely believed to be an inexpensive and scalable way to assess students in large classroom settings. In this paper, we propose &quot;calibrated self-grading&quot; as a more efficient alternative to peer grading. For self-grading, students assign themselves a grade that they think they deserve via an incentive-compatible mechanism that elicits maximally truthful judgements of performance. We show that the students' self-evaluation scores obtained via this mechanism can be used to perform classic item response theory (IRT) analysis. In order to obtain unbiased estimates of the IRT parameters, we show that the self-assigned grades can be calibrated with a minimum amount of input from instructors or domain experts. We demonstrate the effectiveness of the proposed calibrated self-grading approach via simulations and experiments on Amazon's Mechanical Turk. [For the full proceedings, see ED592609.]
Boosted Decision Tree for Q-Matrix Refinement
Author: Xu, Peng
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Matrices,Q Methodology,Psychometrics,Decision Making,Mathematics,Models,Prediction,Validity,Item Response Theory

In recent years, substantial improvements were obtained in the effectiveness of data driven algorithms to validate the mapping of items to skills, or the Q-matrix. In the current study we use ensemble algorithms on top of existing Q-matrix refinement algorithms to improve their performance. We combine the boosting technique with a decision tree. The results show that the improvements from both the decision tree and Adaboost combined are better than the decision tree alone and yield substantial gains over the best performance of individual Q-matrix refinement algorithm. [For the full proceedings, see ED592609.]
Dynamics of Peer Grading: An Empirical Study
Author: de Alfaro, Luca
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Peer Evaluation,Grading,Error Patterns,Accuracy,Correlation,Student Behavior,Academic Ability,College Students,High School Students,Time

Peer grading is widely used in MOOCs and in standard university settings. The quality of grades obtained via peer grading is essential for the educational process. In this work, we study the factors that influence errors in peer grading. We analyze 288 assignments with 25,633 submissions and 113,169 reviews conducted with CrowdGrader, a web based peer grading tool. First, we found that large grading errors are generally more closely correlated with hard-to-grade submission, rather than with imprecise students. Second, we detected a weak correlation between review accuracy and student proficiency, as measured by the quality of the student's own work. Third, we found little correlation between review accuracy and the time it took to perform the review, or how late in the review period the review was performed. Finally, we found a clear evidence of tit-for-tat behavior when students give feedback on the reviews they received. We conclude with remarks on how these data can lead to improvements in peer-grading tools. [For the full proceedings, see ED592609.]
Constraining Issues in Face-to-Face and Internet-Based Language Testing
Author: García Laborda, Jesús
Date: 2018-00-00
Type: Journal Articles
Subjects: Language Tests,Computer Assisted Testing,High Stakes Tests,Internet,Test Anxiety,Foreign Countries,High School Students,College Entrance Examinations,Verbal Tests,Stress Variables,Time Management

Despite the increasing importance of language testing, technology design matters related to testing have hardly been addressed in academic research, yet not enough has been written on test-taking anxiety related to computerized speaking tests. Although students nowadays are digital natives, in our experience they seem to experience a significant level of anxiety when facing Internet-based oral. This research addresses an observational study of 67 students at the University of Alcalá who took a computer-delivered exam. The study concludes that despite students growing up with computers in their lives, we are far from invisible interfaces and independence when it comes to the way an exam is delivered. The results of this research study can shed light on what needs to be revised in computer or internet speaking tasks in high-stakes language tests.
Evaluating the Open Admission Practice in Vocational Colleges (Kirklareli University Case)
Author: Arpacioglu, Isil Tuzun
Date: 2019-00-00
Type: Journal Articles
Subjects: Foreign Countries,Vocational Education,Vocational Schools,College Students,College Admission,Open Enrollment,Program Termination,Outcomes of Education,Educational Quality,Employment Potential,Program Evaluation,College Entrance Examinations

With a regulation made in 2001, vocational and technical secondary education graduates were given the right to enroll to vocational colleges without taking the national central exam. However, this regulation was repealed in 2016. If the students are placed in an associate degree program in the same field, additional points are added to the points gained from central examination. The purpose of this research is to evaluate the open admission practice which had been going on for more than fifteen years and the abolition of this practice according to the opinions of the academics who work in vocational colleges of Kirklareli University. The data of the research were collected with the help of four open ended questions in written form, with the qualitative research method. The abolition of the open admission practice is evaluated as a positive development by academic stuff of Kirklareli University, in terms of the quality of education and employability. The research findings show that eliminating the open admission practice is a positive practice. According to the findings of the study, was evaluated positively the removal of the open admission practice. Inter-agency cooperation on the issue can be useful. [Presented at ERPA International Congress on Education 2018, on 28 June 2018, in Istanbul.]
The Education of Syrian Children in Terms of Functional and Spiritual Social Integration: A Theoretical Analysis
Author: Zabun, Beyhan
Date: 2018-10-00
Type: Journal Articles
Subjects: Foreign Countries,Refugees,Spiritual Development,Social Integration,Socialization,Children,Cultural Background,Inclusion,Child Development,Citizenship Education

In the current study, the formal education of the Syrian children living in Turkey who are in the position of refugees under temporary protection was investigated sociologically in terms of the two basic dimensions of social integration. Social integration are regarded in two dimensions as functional and spiritual. According to official data, there are almost 4 million Syrians living in Turkey. The integration of 4 million people to the social structure is a hard process with all its dimensions. Education is the most important instrument of the Integration process. One of the significant functions of formal education is to make the individual a member of community. Being a sociological phenomenon, integration corresponds with the social integration and the objectives of education. While socialization and cultural heritage transfer, as the objectives of education, aims at spiritual integration, the purpose of political function, economic function and the function of developing the individual are functional integration.
Classifying Behavior to Elucidate Elegant Problem Solving in an Educational Game
Author: Malkiewich, Laura
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Classification,Problem Solving,Educational Games,Models,Prediction,Student Behavior,Physics,Middle School Students,High School Students,Grade 8,Grade 9

Educational games have become hugely popular, and educational data mining has been used to predict student performance in the context of these games. However, models built on student behavior in educational games rarely differentiate between the types of problem solving that students employ and fail to address how efficacious student problem solutions are in game environments. Furthermore, few papers assess how the features selected for classification models inform an understanding of how student behaviors predict student performance. In this paper, we discuss the creation and consideration of two models that predict if a student will develop an elegant problem solution (the Gold model), or a non-optimal but workable solution (the Silver model), in the context of an educational game. A pre-determined set of features were systematically tested and fit into one or both of these models. The two models were then examined to understand how the selected features elucidate our understanding of student problem solving at varying levels of sophistication. Results suggest that while gaming the system and lack of persistence indicate non-optimal completion of a problem, gaining experience with a problem predicts more elegant problem solving. Results also suggest that general student behaviors are better predictors of student performance than level-specific behaviors. [For the full proceedings, see ED592609.]
Choosing Teaching English as a Profession in Primary Education Contexts
Author: Amengual Pizarro, Marian
Date: 2017-00-00
Type: Journal Articles
Subjects: Preservice Teachers,Student Teacher Attitudes,English (Second Language),Second Language Learning,Second Language Instruction,Self Concept,Correlation,Oral Language,Language Skills,Gender Differences,Job Security,Foreign Countries,Altruism,Career Choice,Language Teachers,Language Proficiency,Mastery Learning,Elementary School Teachers

The main aim of this study was to examine the most relevant motivating factors (i.e. intrinsic, extrinsic and altruistic) influencing pre-service English school teachers to choose teaching as their profession. A small questionnaire was administered to 73 third-year student teachers at the University of the Balearic Islands (UIB). Results indicate that integrative reasons were the most dominant to choose teaching for both male and female students, followed next by altruistic reasons. Extrinsic reasons such as job security or lengthy holidays seem to have the least impact upon participants' decision to enter the profession. However, results reveal statistically significant differences across motivational reasons within each category as a function of gender. Findings also indicate that participants show a highly positive self-concept regarding their English proficiency. This latter affective variable was found to be positively correlated with participants' mastery of oral skills.
Choosing versus Receiving Feedback: The Impact of Feedback Valence on Learning in an Assessment Game
Author: Cutumisu, Maria
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Feedback (Response),Educational Games,Computer Assisted Testing,Selection,Adults,Time,Gender Differences,Outcomes of Education

Studies examining feedback in educational settings have largely focused on feedback that is received, rather than chosen, by students. This study investigates whether adult participants learn more from choosing rather than receiving feedback from virtual characters in a digital poster design task. We employed a yoked study design and two versions of an online game-based assessment, Posterlet, to compare the learning outcomes of N=264 Mechanical Turk adults in two conditions: when they &quot;chose&quot; the feedback valence versus when they &quot;received&quot; the same feedback valence and order. In Posterlet, players design posters and learn graphic design principles from feedback. We found that the more the participants chose critical feedback, the more time they spent designing posters, but there were no differences in learning, revision, and time spent designing posters between conditions. In each condition, critical feedback correlated with performance and revision, suggesting that feedback valence is important for performance, regardless of being a choice. [For the full proceedings, see ED592609.]
21st Century English Primary Teachers and Technology
Author: García Laborda, Jesús
Date: 2017-00-00
Type: Journal Articles
Subjects: Elementary School Teachers,Teaching Methods,Student Motivation,Foreign Countries,English (Second Language),Second Language Learning,Second Language Instruction,Educational Technology,Technology Integration,Teacher Attitudes

Being a teacher is much more that delivering contents in the classroom. It implies setting standard behaviors as well as showing ways to work. In one word, teachers are an example in the classroom. The implementation of their way to teach will be considered as good or bad teaching by their own students for many years. Likewise, the way they use technology in the classroom can trigger motivation and perceptions in their future students. That is why, teachers need to be conscious of what they do in the classroom and adopt technology principles to be applied in a near future. This paper addresses how teachers do that at Universidad de Alcalá (Madrid, Spain). The paper shows the main work done in class and considers the effect in the short and long run.
Deep Learning + Student Modeling + Clustering: A Recipe for Effective Automatic Short Answer Grading
Author: Zhang, Yuan
Date: 2016-00-00
Type: Reports - Research
Subjects: Automation,Grading,Artificial Intelligence,Test Format,Models,Classification,College Students,College Science,Physics,Intelligent Tutoring Systems

In this work we tackled the task of Automatic Short Answer Grading (ASAG). While conventional ASAG research makes prediction mainly based on student answers referred as Answer-based, we leveraged the information about questions and student models into consideration. More specifically, we explore the Answer-based, Question, and Student models individually, and subsequently in various combined and composite models through feature engineering. Additionally, we extend the exploration of machine learning methods by utilizing Deep Belief Networks (DBN) together with other five classic classifiers. Our experimental results show that our proposed feature engineering models significantly out-performed the conventional Answer-based model and among the six machine learning classifiers, DBN is the best followed by SVM, and Naive Bayes is the worst. [For the full proceedings, see ED592609.]
Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming
Author: Paaßen, Benjamin
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Programming,Computer Science Education,Data,Coding,Classification

The first intelligent tutoring systems for computer programming have been proposed more than 30 years ago, mostly focusing on well defined programming tasks e.g. in the context of logic programming. Recent systems also teach complex programs, where explicit modelling of every possible program and mistake is no longer possible. Such systems are based on data-driven approaches, which focus on the syntax of a program or consider the output for example cases. However, the system's understanding of student programs could be enriched by a deeper focus on the actual execution of a program. This requires a suitable data representation which encodes information of programming style as well as its functionality in a suitable way, thus offering entry points for automated feedback generation. In this contribution we propose a representation of computer programs via execution traces for example input and demonstrate the power of this representation in three key challenges for intelligent tutoring systems: identifying the underlying solution strategy, identifying erroneous solutions and locating the errors in erroneous programs for feedback display. [For the full proceedings, see ED592609.]
Document Segmentation for Labeling with Academic Learning Objectives
Author: Bhartiya, Divyanshu
Date: 2016-00-00
Type: Reports - Research
Subjects: Behavioral Objectives,Instructional Materials,Computer Uses in Education,Classification,Metadata,Automation

Teaching in formal academic environments typically follows a curriculum that specifies learning objectives that need to be met at each phase of a student's academic progression. In this paper, we address the novel task of identifying document &quot;segments&quot; in educational material that are relevant for different learning objectives. Using a dynamic programming algorithm based on a vector space representation of sentences in a document, we automatically segment and then label document segments with learning objectives. We demonstrate the effectiveness of our approach on a real-world education data set. We further demonstrate how our system is useful for related tasks of document passage retrieval and QA using a large publicly available dataset. To the best of our knowledge we are the first to attempt the task of segmenting and labeling education materials with academic learning objectives. [For the full proceedings, see ED592609.]
Gauging MOOC Learners&apos; Adherence to the Designed Learning Path
Author: Davis, Dan
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Large Group Instruction,Online Courses,Educational Technology,Technology Uses in Education,Learning Processes,Student Behavior,Learner Engagement,Academic Persistence,Visualization,Video Technology,Interaction

Massive Open Online Course (MOOC) platform designs, such as those of edX and Coursera, afford linear learning sequences by building scaffolded knowledge from activity to activity and from week to week. We consider those sequences to be the courses' &quot;designed&quot; learning paths. But do learners actually adhere to these designed paths, or do they forge their own ways through the MOOCs? What are the implications of either following or not following the designed paths? Existing research has greatly emphasized, and succeeded in, automatically predicting MOOC learner success and learner dropout based on behavior patterns derived from MOOC learners' data traces. However, those predictions do not directly translate into practicable information for course designers and instructors aiming to improve engagement and retention -- the two major issues plaguing today's MOOCs. In this work, we present a three-pronged approach to exploring MOOC data for novel learning path insights, thus enabling course instructors and designers to adapt a course's design based on empirical evidence. [For the full proceedings, see ED592609.]
Does a Peer Recommender Foster Students&apos; Engagement in MOOCs?
Author: Labarthe, Hugues
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Learner Engagement,Peer Relationship,Academic Persistence,Computer Uses in Education,Automation,Program Effectiveness,Attendance,Student Participation

Overall the social capital of MOOCs is under-exploited. For most students in MOOCs, autonomous learning often means learning alone. Students interested in adding a social dimension to their learning can browse discussion threads, join social medias and may message other students but usually in a blind and somehow random way, only hoping to find someone relevant, available and also willing to interact. This common isolation might be a contributing factor on student attrition rate and on their general learning experience. To foster learners' persistence in MOOCs, we propose to enhance the MOOC experience with a recommender which provides each student with an individual list of rich-potential contacts, created in real-time on the basis of their own profile and activities. This paper describes a controlled study conducted from Sept. to Nov. 2015 during a MOOC on Project Management. A recommender panel was integrated to the experimental users' interface and allowed them to manage contacts, send them an instant message or consult their profile. The population (N = 8,673) was randomly split into two: a control group, without any recommendations, and an experimental group in which students could choose to activate and use the recommender. After having demonstrated that these populations were similar up to the activation of the recommender, we evaluate the effect of the recommender on the basis of four factors of learners' persistence: attendance, completion, success and participation. Results show the recommender improved all these 4 factors: students were much more likely to persist and engage in the MOOC if they received recommendations than if they did not. [For the full proceedings, see ED592609.]
Effect of Student Ability and Question Difficulty on Duration
Author: Ma, Yijun
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Difficulty Level,Reaction Time,Student Reaction,Academic Ability,Test Items,Electronic Learning,Item Response Theory,Knowledge Level,Academic Achievement

Time has become a standard feature used in EDM models, and is used in models of meta-cognitive strategies to models of disengagement. Most of these models consider whether a student action is &quot;too fast&quot; or &quot;too slow&quot;. However, an open question remains on how we define and select these cut-offs. Moreover, it is not clear that the same cut-offs are appropriate across different situations. Some students may generally respond faster than others; more difficult items may take different amounts of time. In this paper, we consider whether absolute or relative indicators of time are more appropriate as cut-offs, and whether simple transformations (such as log time) are useful when representing time. We do so through visualizing student performance in relation to general student ability, item difficulty, and different ways of representing time. We find that student knowledge and item difficulty should be taken into account when choosing cut-offs, and that there are advantages to representing duration in terms of standardized log-time. [For the full proceedings, see ED592609.]