ethanporcaro/eric-fulltext-meta huggingface dataset:
Collaborative Problem-Solving Process in a Science Serious Game: Exploring Group Action Similarity Trajectory
Author: Kang, Jina
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Cooperative Learning,Problem Solving,Science Education,Educational Games,Learning Processes,Middle School Students,Grade 6,Cluster Grouping,Sequential Approach

Collaborative problem-solving (CPS) as a key competency required in the 21st century. There has been an increasing need to understand CPS since it involves not only cognitive but also social processes, and thus its process is difficult to examine. Recent research has highlighted that computer-based learning environments provide an opportunity for students to collaborate with others to solve scientific problems and facilitate their knowledge building process, which can be dynamically tracked within the systems. However, limited research has attempted to identify CPS process captured in the computer-based learning environments designed for supporting CPS. This study therefore aimed to investigate students' CPS process in a serious game, &quot;Alien Rescue,&quot; by analyzing a student's daily tool use action sequence generated in the game. First, we computed a daily gameplay action similarity among students in a group using a similarity coefficient, &quot;Jaccard&quot; (&quot;Jac&quot;). Each group's &quot;Jac&quot; coefficients over the entire gameplay period (i.e. six days over three weeks) were considered as the group action similarity trajectory. The &quot;Jac&quot; coefficient of each day was entered as a single feature (i.e. a total of six features) to conduct a &quot;KmL&quot; cluster analysis that clusters longitudinal data. Three clusters of groups with similar behavior traits (i.e. group action similarity trajectories) were identified. The groups' background information (e.g. solution scores, knowledge gain scores) further provided how the groups' CPS traits can be related to their learning performance. [For the full proceedings, see ED599096.]
A Self-Attentive Model for Knowledge Tracing
Author: Pandey, Shalini
Date: 2019-07-00
Type: Reports - Descriptive
Subjects: Learning Processes,Databases,Intelligent Tutoring Systems,Knowledge Level,Academic Achievement,Prediction,Models,Attention,Students

Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized learning platform to students. In recent years, methods based on Recurrent Neural Networks (RNN) such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) outperformed all the traditional methods because of their ability to capture a complex representation of human learning. However, these methods face the issue of not generalizing well while dealing with sparse data which is the case with real-world data as students interact with few KCs. In order to address this issue, we develop an approach that identifies the KCs from the student's past activities that are &quot;relevant&quot; to the given KC and predicts his/her mastery based on the relatively few KCs that it picked. Since predictions are made based on relatively few past activities, it handles the data sparsity problem better than the methods based on RNN. For identifying the relevance between the KCs, we propose a self-attention based approach, Self Attentive Knowledge Tracing (SAKT). Extensive experimentation on a variety of real-world dataset shows that our model outperforms the state-of-the-art models for knowledge tracing, improving AUC by 4.43% on average. [For the full proceedings, see ED599096.]
Educational System Building in a Changing Educational Sector: Environment, Organization, and the Technical Core
Author: Spillane, James P.
Date: 2019-08-20
Type: Reports - Research
Subjects: Educational Improvement,Educational Policy,Administrator Role,Accountability,Standards,Elementary Secondary Education,Educational Environment,Montessori Schools,Urban Schools,Advanced Placement Programs,Catholic Schools,Public Schools,Private Schools,Charter Schools,Suburban Schools

The institutional environment of US school systems has changed considerably over a quarter century as standards and test-based accountability became central ideas in both policy texts and discourses about improving education. We explore how US school systems are managing in this changed environment by focusing on school system leaders' sense-making about their environments as they attempt to build education systems in order to improve instruction, the core technology of schooling. We identify the policy texts and discourses system leaders notice and their framings, interpretations, and uses of these cues as they work on building educational infrastructures to support more coherent instructional visions. We argue that school systems' educational infrastructure building efforts were intended at coupling their systems' formal organization with particular environmental cues in an effort to influence classroom instruction. In turn, we argue that these educational infrastructure building efforts can simultaneously be motivated by, and in pursuit of, both institutional ritual and technical rationality. [This paper was published in &quot;Educational Policy&quot; v33 n6 p845-881 2019 (EJ1225591).]
Clustering Students Based on Their Prior Knowledge
Author: Khayi, Nisrine Ait
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: High School Students,Cluster Grouping,Prior Learning,Intelligent Tutoring Systems,Student Evaluation,Computation,Science Education,Scientific Concepts,Problem Solving,Physics,Science Tests

In this paper, we applied a number of clustering algorithms on pretest data collected from 264 high-school students. Students took the pre-test at the beginning of a 5-week experiment in which they interacted with an intelligent tutoring system. The primary goal of this work is to identify clusters of students exhibiting similar knowledge patterns. In particular, we show that the DP-means clustering algorithm yields very good results using binary response data. Other clustering algorithms such as k-modes have demonstrated better results when using categorical response data. [For the full proceedings, see ED599096.]
Assessing Student Response in Tutorial Dialogue Context Using Probabilistic Soft Logic
Author: Banjade, Rajendra
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Dialogs (Language),Probability,Intelligent Tutoring Systems,Semantics,Knowledge Level,Difficulty Level,Responses,Two Year College Students,Interaction,Logical Thinking,Models,Questioning Techniques

Automatic answer assessment systems typically apply semantic similarity methods where student responses are compared with some reference answers in order to access their correctness. But student responses in dialogue based tutoring systems are often grammatically and semantically incomplete and additional information (e.g., dialogue history) is needed to better assess their correctness. In that, we have proposed augmenting semantic similarity based models with, for example, knowledge level of the student and question difficulty and jointly modeled their complex interactions using Probabilistic Soft Logic (PSL). The results of the proposed PSL models to infer the correctness of the given answer on DT-Grade dataset show the more than 7% improvement on accuracy over the results obtained using a semantic similarity model. [For the full proceedings, see ED599096.]
Categorizing Students&apos; Questions Using an Ensemble Hybrid Approach
Author: Harrak, Fatima
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Classification,Questioning Techniques,Artificial Intelligence,Documentation,Automation,Medical Students,Foreign Countries

Students' questions categorization is a challenging task as the available corpora are often limited in size (particularly with languages other than English) and require a costly preliminary manual annotation to train the classifiers. Ensemble learning can help improve machine learning results by combining several models, and is particularly efficient to leverage the strengths of very different classifiers. In this paper, we investigate how combining a rule-based annotator (based on keywords identified by an expert) with various machine learning-based approaches and TF-IDF can improve the automated identification of questions asked by 1st year medicine students on an online platform, according to a coding scheme using 4 dimensions. First we evaluated the performance of several models, calculating the kappa between the prediction and the manually labelled dataset, according to each dimension. Then, using a stacking approach, we tried different combinations of them to design a predictive model with a higher performance. The results reveal that the new ensemble models can help to increase the performance for all dimensions of the dataset, in particular those for which the expert rule-based system showed the lowest performance. These results are promising as they indicate that some easy-to-train models can complement more manual approaches, even with a small training set of a few hundreds of annotated questions. [For the full proceedings, see ED599096.]
Design and Evaluation of a Semantic Indicator for Automatically Supporting Programming Learning
Author: Broisin, Julien
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Semantics,Programming,Syntax,Feedback (Response),Misconceptions,Problem Solving,Introductory Courses,Computer Science Education,Classification,Correlation,Summative Evaluation,Accuracy,Intelligent Tutoring Systems,Teaching Methods,Online Courses,Foreign Countries

How to support students in programming learning has been a great research challenge in the last years. To address this challenge, prior works have mainly focused on proposing solutions based on syntactic analysis to provide students with personalized feedback about their grammatical programming errors and misconceptions. However, syntactic analysis falls short on informing learners how they solve the programming problem, even if one key learning outcome of programming relates to the development of an individual's ability to solve a problem. In this article, we introduce an indicator to analyze beginners' code based on semantic proximity. This indicator adapts an edit distance algorithm (i.e., the Levenshtein distance) to express the proximity of the students' code with the expected solution provided by the teacher, in order to express the learners' capacity to solve the given problem. To process our indicator, we applied machine learning techniques to a dataset from an introductory programming course with a sample of 166 students. The first results are encouraging. On the one hand, the semantic indicator can be used to automatically classify source codes as semantically correct or incorrect in 58% of the cases. On the other hand, the indicator is correlated with teachers' summative evaluations of students' codes. Even if further investigations must be conducted to improve the indicator's accuracy, the results of this study make it possible to use our approach as the foundations for future research in semanticbased intelligent and awareness programming systems. [For the full proceedings, see ED599096.]
Characterising Students&apos; Writing Processes Using Temporal Keystroke Analysis
Author: Malekian, Donia
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Writing Processes,Keyboarding (Data Entry),Writing (Composition),Writing Evaluation,Undergraduate Students,Business Administration Education,Foreign Countries

This work aims to characterize students' writing processes using keystroke logs and understand how the extracted characteristics influence the text quality at specific moments of writing. Earlier works have proposed predictive models characterizing students' writing processes and mainly rely on distribution-based measures of pauses obtained from the overall keystroke logs. However, the effect of isolated phases of writing has not been evaluated in these models. Moreover, current theories on writing suggest that the quality of writing depends on when specific writing behaviours are performed. This view is not examined in the keystroke logging analysis literature. Addressing the mentioned challenges, the two contributions of this work are: a) characterizing students' writing processes connected to isolated writing phases and examining their influence on writing quality; and b) temporal analysis of keystrokes and investigating whether the significance of writing characteristics varies as students progress in their writing task. Our results suggest that characterizing students' writing based on isolated writing phases is slightly more predictive of writing quality. Additionally, the effect of several writing characteristics on writing quality changes when considering the time dimension. [For the full proceedings, see ED599096.]
Concept-Aware Deep Knowledge Tracing and Exercise Recommendation in an Online Learning System
Author: Ai, Fangzhe
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Independent Study,Grade 5,Elementary School Students,Foreign Countries,Models,Performance,Knowledge Level,Correlation,Intelligent Tutoring Systems,Learning Activities,Assignments,Mathematics Instruction,Reinforcement,Mathematics

Personalized education systems recommend learning contents to students based on their capacity to accelerate their learning. This paper proposes a personalized exercise recommendation system for online self-directed learning. We first improve the performance of knowledge tracing models. Existing deep knowledge tracing models, such as Dynamic Key-Value Memory Network (DKVMN), ignore exercises' concept tags, which are usually available in tutoring systems. We modify DKVMN to design its memory structure based on the course's concept list, and explicitly consider the exercise-concept mapping relationship during students' knowledge tracing. We evaluated the model on the 5th grade students' math exercising dataset in TAL, one of the biggest education groups in China, and found that our model has higher performance than existing models. We also enhance the DKVMN model to support more input features and obtain higher performance. Second, we use the model to build a student simulator, and use it to train an exercise recommendation policy with deep reinforcement learning. Experimental results show that our policy achieves better performance than existing heuristic policy in terms of maximizing the students' knowledge level. To the best of our knowledge, this is the first time that deep reinforcement learning has been applied to personalized mathematic exercise recommendation. [For the full proceedings, see ED599096.]
Content-Based Course Recommender System for Liberal Arts Education
Author: Morsomme, Raphaël
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Liberal Arts,Course Selection (Students),Courses,College Students,Decision Making,Foreign Countries,Data Collection,Models,Grade Point Average,Student Characteristics,Predictor Variables,Planning,Value Judgment

Liberal Arts programs are often characterized by their open curriculum. Yet, the abundance of courses available and the highly personalized curriculum are often overwhelming for students who must select courses relevant to their academic interests and suitable to their academic background. This paper presents the course recommender system that we have developed for the Liberal Arts bachelor of the University College Maastricht, the Netherlands. It aims to complement academic advising and help students make better-informed course selections. The system recommends courses whose content best matches the student's academic interests, issues warnings for courses that are too advanced given the student's academic background and, in the latter case, suggests suitable preparatory courses. We base the course recommendations on a topic model fitted on course descriptions, and the warnings on a sparse predictive model for grade based on students' past academic performance and level of academic expertise. Preparatory courses consist of courses whose content has the best preparatory value according to the predictive model. We find that course recommendations are relevant for a wide range of academic interests present in the student population and that students found recommendations for courses at other departments especially helpful. The preparatory courses often lack coherence with the target course and need to be improved. [For the full proceedings, see ED599096.]
Do Learners Know What&apos;s Good for Them? Crowdsourcing Subjective Ratings of OERs to Predict Learning Gains
Author: Whitehill, Jacob
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Open Educational Resources,Electronic Publishing,Collaborative Writing,Web Sites,Student Attitudes,Achievement Gains,Problem Solving,Inferences,Educational Quality

We explored how learners' &quot;subjective ratings&quot; of open educational resources (OERs) in terms of how much they find them &quot;helpful&quot; can predict the actual &quot;learning gains&quot; associated with those resources as measured with pre- and post-tests. To this end, we developed a probabilistic model called GRAM (Gaussian Rating Aggregation Model) that combines subjective ratings from multiple learners into an aggregate quality score of each resource. Based on an experiment we conducted on Mechanical Turk (n = 304 participants with m = 17 math tutorial videos as resources), we found that aggregated subjective ratings are highly (and stat. sig.) predictive of the resources' average learning gains, with Pearson correlation of 0.78. Moreover, when predicting average learning gains of &quot;new&quot; learners, subjective scores were still predictive (Pearson correlation of 0.49) and attained higher prediction accuracy than a model that directly uses pre- and post-test data to estimate learning gains for each resource. These results have potential implications for large-scale learning platforms (e.g., MOOCs, Khan Academy) that assign resources (tutorials, explanations, hints, etc.) to learners based on the expected learning gains. [For the full proceedings, see ED599096.]
PARCC Final Technical Report for 2016 Administration
Author: Partnership for Assessment of Readiness for College and Careers (PARCC)
Date: 2017-01-10
Type: Numerical/Quantitative Data
Subjects: College Readiness,Career Readiness,Common Core State Standards,Language Arts,Mathematics,Grade 3,Grade 4,Grade 5,Grade 6,Grade 7,Grade 8,Grade 9,Grade 10,Grade 11,Academic Achievement,Scores,Testing,Test Construction,Scoring,Student Characteristics,Item Analysis,Test Bias,Test Reliability,Item Response Theory,Scaling,Performance,Quality Control,Testing Accommodations,Plagiarism,Automation,Test Validity,Cheating

The Partnership for Assessment of Readiness for College and Careers (PARCC) is a state-led consortium designed to create next-generation assessments that, compared to traditional K-12 assessments, more accurately measure student progress toward college and career readiness. The PARCC assessments are aligned to the Common Core State Standards (CCSS) and were administered operationally for the first time in the 2014-2015 academic year. PARCC comprises assessments in both English Language Arts/Literacy (ELA/L) and Mathematics in grades 3 to 8 and high school. The information provided in this technical report is intended for use by those who evaluate tests, interpret scores, or use test results in making educational decisions. It is assumed that the reader has technical knowledge of test construction and measurement procedures, as stated in &quot;Standards for Educational and Psychological Testing&quot; (American Educational Research Association [AERA], American Psychological Association [APA], &amp; National Council on Measurement in Education [NCME], 2014). The purpose of this technical report is to describe the second operational administration of the PARCC assessments in the 2015-2016 academic year and includes the following topics: (1) Background and purpose of the assessments; (2) Test development of items and forms; (3) Test administration, security, and scoring; (4) Test taker characteristics; (5) Classical item analyses and differential item functioning; (6) Reliability and validity of scores; (7) Item response theory (IRT) calibration and scaling; (8) Performance level setting; (9) Development of the score reporting scales and student performance; and (10) Quality control procedures.
PARCC Final Technical Report for 2018 Administration
Author: Partnership for Assessment of Readiness for College and Careers (PARCC)
Date: 2019-02-28
Type: Numerical/Quantitative Data
Subjects: College Readiness,Career Readiness,Common Core State Standards,Language Arts,Mathematics,Grade 3,Grade 4,Grade 5,Grade 6,Grade 7,Grade 8,Grade 9,Grade 10,Grade 11,Academic Achievement,Scores,Testing,Test Construction,Scoring,Student Characteristics,Item Analysis,Test Bias,Test Reliability,Item Response Theory,Scaling,Performance,Quality Control,Testing Accommodations,Plagiarism,Automation,Test Validity,Cheating

The Partnership for Assessment of Readiness for College and Careers (PARCC) is a state-led consortium designed to create next-generation assessments that, compared to traditional K-12 assessments, more accurately measure student progress toward college and career readiness. The PARCC assessments are aligned to the Common Core State Standards (CCSS) and were administered operationally for the first time in the 2014-2015 academic year. PARCC comprises assessments in both English Language Arts/Literacy (ELA/L) and Mathematics in grades 3 to 8 and high school. The information provided in this technical report is intended for use by those who evaluate tests, interpret scores, or use test results in making educational decisions. It is assumed that the reader has technical knowledge of test construction and measurement procedures, as stated in &quot;Standards for Educational and Psychological Testing&quot; (American Educational Research Association [AERA], American Psychological Association [APA], &amp; National Council on Measurement in Education [NCME], 2014). The purpose of this technical report is to describe the first operational administration of the PARCC assessments and includes the following topics: (1) Background and purpose of the assessments; (2) Test development of items and forms; (3) Test administration, security, and scoring; (4) Test taker characteristics; (5) Classical item analyses and differential item functioning; (6) Reliability and validity of scores; (7) Item response theory (IRT) calibration and scaling; (8) Performance level setting; (9) Development of the score reporting scales and student performance; (10) student growth measures; and (11) Quality control procedures.