ethanporcaro/eric-fulltext-meta huggingface dataset:
Neural Recall Network: A Neural Network Solution to Low Recall Problem in Regex-Based Qualitative Coding
Author: Cai, Zhiqiang
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Coding,Classification,Artificial Intelligence,Engineering Education,Design,Models,Error Patterns

Regular expression (regex) coding has advantages for text analysis. Humans are often able to quickly construct intelligible coding rules with high precision. That is, researchers can identify words and word patterns that correctly classify examples of a particular concept. And, it is often easy to identify false positives and improve the regex classifier so that the positive items are accurately captured. However, ensuring that a regex list is complete is a bigger challenge, because the concepts to be identified in data are often sparsely distributed, which makes it difficult to identify examples of &quot;false negatives.&quot; For this reason, regex-based classifiers suffer by having low recall. That is, it often misses items that should be classified as positive. In this paper, we provide a neural network solution to this problem by identifying a &quot;negative reversion set,&quot; in which false negative items occur much more frequently than in the data set as a whole. Thus, the regex classifier can be more quickly improved by adding missing regexes based on the false negatives found from the negative reversion set. This study used an existing data set collected from a simulation-based learning environment for which researchers had previously defined six codes and developed classifiers with validated regex lists. We randomly constructed incomplete (partial) regex lists and used neural network models to identify negative reversion sets in which the frequency of false negatives increased from a range of 3%-8% in the full data set to a range of 12%-52% in the negative reversion set. Based on this finding, we propose an interactive coding mechanism in which human-developed regex classifiers provide input for training machine learning algorithms and machine learning algorithms &quot;smartly&quot; select highly suspected false negative items for human to more quickly develop regex classifiers. [For the full proceedings, see ED623995.]
A Narrative Discourse of a Cross-Cultural Study Experience in a Chinese University
Author: Djiraro Mangue, Célestine Laure
Date: 2022-00-00
Type: Journal Articles
Subjects: Personal Narratives,Acculturation,Foreign Countries,Study Abroad,Cultural Awareness,Student Adjustment,Best Practices,Barriers,Second Language Learning,Teacher Student Relationship,Communication Problems,Peer Relationship,Cultural Differences,Educational Experience,Figurative Language,Language Usage,Discourse Analysis,Foreign Students,Political Science,Doctoral Programs,Doctoral Students,Asian Culture

In this paper, through a personal narrative discourse, I explore the influence and the contribution of my cross-cultural study experience in China on my development as a young researcher. Through the Chinese academic culture, I learned some best practices that I consider essential for the blossoming and development of a student. Nonetheless, I also face some challenges such as language problems, communication with teachers, and even with classmates. Despite the issue of language and cultural disparities that were significant upon my arrival, I have managed to adapt and learn from Chinese society, teachers, and students. Also, the challenges that I had to overcome were not comparable to the knowledge acquired, and the rich experience that I have accumulated throughout the years spent in China. Through this personal narrative discourse, the different metaphors used to depict our academic life experience in China can enable the reader to have a kind of educative outlook.
Volunteering, Educational Attainment, and Literacy Skills among Middle-Aged and Older Adults by Racial and Ethnic Groups in the U.S.
Author: Narine, Donnette
Date: 2022-10-13
Type: Reports - Research
Subjects: Volunteers,Role of Education,Adult Literacy,Adults,Older Adults,Racial Differences,Ethnicity,Whites,African Americans,Hispanic Americans,Correlation,Educational Attainment,Human Capital,Cultural Capital,Social Capital

Volunteer participation benefits societies and individuals. The objective of this study was to examine the roles of education and adult literacy in formal volunteering among a nationally representative sample of the U.S. middle-aged and older adults (45-74 years) by race and ethnicity (Whites, Blacks, Hispanics). Using cross-sectional data (n = 3,770) from the 2012/2014/2017 Program for the International Assessment of Adult Competencies (PIAAC), structural equation models were used to determine mediation relationships among educational attainment, literacy, and volunteering. The effect of educational attainment on formal volunteering was significantly greater among middle-aged and older Black adults compared to their Hispanic counterparts, as well as among middle-aged and older White adults compared to their Hispanic counterparts. However, literacy was the education-volunteering mediator among White adults only. Suggested policy implications include support for volunteer participation through culturally and socioeconomically sensitive approaches along with human capital development in adult life stages. [This paper was published in &quot;Educational Gerontology&quot; Oct 2022.]
Do Teacher Beliefs Mediate the Relationship between Professional Development and Reading Outcomes of Students with Emotional and Behavioral Disorders? An Exploration of Effects from a Randomized Controlled Trial
Author: Filderman, Marissa J.
Date: 2022-10-12
Type: Reports - Research
Subjects: Elementary School Teachers,Faculty Development,Behavior Disorders,Emotional Disturbances,Reading Achievement,Beliefs,Self Concept,Social Emotional Learning,Questionnaires,Self Concept Measures,Self Efficacy,Elementary School Students,Emergent Literacy,Reading Fluency,Reading Tests

Teachers of students with emotional and behavioral disorders face unique challenges in the classroom that are often not addressed in teacher preparation, which may result in diminished outcomes for teachers and students. The Integrated Literacy Study Groups professional development was designed to support the needs of teachers of students with or at-risk for emotional and behavioral disorders by integrating components of social-emotional learning and literacy instruction. The present study uses structural equation modeling to evaluate how and to what extent teacher beliefs, targeted in training through collaborative work groups and coaching experiences, mediate the relationship between the Integrated Literacy Study Groups and student reading achievement. Among a sample of 74 elementary school teachers, we found a directional relationship such that training influenced: (1) beliefs pertaining to action; (2) beliefs pertaining to self; and (3) student reading outcomes. Beliefs pertaining to self was a significantly stronger mediator of the relationship between professional development and student reading outcomes. We discuss potential reasons for these findings as well as their implications for the design of training of teachers of students with or at-risk for emotional and behavioral disorders. [This is the online version of an article published in &quot;Social Psychology of Education.&quot;]
Using Community-Based Problems to Increase Motivation in a Data Science Virtual Internship
Author: Johnson, Jillian C.
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Data,Data Analysis,Interdisciplinary Approach,Student Motivation,Language Usage,Computational Linguistics,Internship Programs,Electronic Learning,College Students,Student Attitudes

Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. Will introducing real-life data science problems fill this motivational deficit? To analyze this question, we contrasted learning with generic datasets and artificial problems (Phase 1) with a community-sourced dataset and authentic problems (Phase 2) in the context of an 8-week virtual internship. Retrospective survey questions indicated interns experienced increased motivation in Phase 2. Additionally, analysis of intern discourse using Linguistic Inquiry and Word Count (LIWC) indicated a significant difference in linguistic measures between the two phases. Phase 1 had significantly greater measures of pronouns with a small-medium effect size, 2nd person words with a medium-large effect size, positive emotion with a medium effect size, inter-rogations with a medium-large effect size, question marks with a medium-large effect size, risk with a medium-large effect size, and causal words with a medium effect size. These results in conjunction with a retrospective survey suggest that phase 1 had more questions asked, more causal relationships defined, and included linguistic features of success and failure. Results from Phase 2 indicated that community-sourced data and problems may increase motivation for learning data science. [For the full proceedings, see ED623995.]
Differences in Teacher Background and School Structure by Racial/Ethnic Congruence between Teachers and Students. CEME Technical Report. CEMETR-2021-04
Author: Lambert, Richard G.
Date: 2021-04-00
Type: Speeches/Meeting Papers
Subjects: Teacher Background,Racial Differences,African American Teachers,Minority Group Teachers,White Teachers,Student Characteristics,Elementary School Teachers,Secondary School Teachers,Hispanic Americans,Minority Group Students,Teacher Characteristics,Teacher Qualifications,Institutional Characteristics,Racial Composition

Differences in personal characteristics, professional qualifications, and contextual factors for congruent and incongruent Black, Hispanic, and White teachers were investigated using data from the National Teacher and Principal Survey (n[subscript teachers]=25,420; n[subscript schools]=5,500). Racial/ethnic congruence was defined as the match between the predominant race/ethnicity of students within a school and the teacher's own race/ethnicity. White incongruent elementary and secondary teachers were less qualified than their congruent counterparts. There were few differences in teacher qualifications between congruent and incongruent Black and Hispanic teachers. This study demonstrates that predominantly White schools employ more qualified teachers given the substantial differences between White congruent and incongruent teachers and thereby illustrates yet another aspect of systemic inequities in U.S. schools. [This paper was presented at the Annual Meeting of the American Educational Research Association.]
Towards Including Instructor Features in Student Grade Prediction
Author: Ong, Nathan
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Prediction,Grades (Scholastic),Learning Analytics,Student Characteristics,Artificial Intelligence,Undergraduate Students,Futures (of Society),Academic Achievement,Models,Comparative Analysis,Teacher Student Relationship,College Faculty,State Universities,Computer Science Education,Correlation,Grade Point Average

Student grade prediction is a popular task for learning analytics, given grades are the traditional form of student performance. However, no matter the learning environment, student background, or domain content, there are things in common across most experiences in learning. In most previous machine learning models, previous grades are considered the strongest prognosis of future performance. Few works consider the breadth of instructor features, despite the evidence that a great instructor could change the course of a student's future. We strive to determine the true impact of an instructor by analyzing student data from an undergraduate program and measuring the importance of instructor-related features in comparison with other feature types that may affect state-of-the-art student grade prediction models. We show that adding extensive instructor-related features improves grade prediction, when using the best supervised learning classifier and regressor. [For the full proceedings, see ED623995.]
SQL-DP: A Novel Difficulty Prediction Framework for SQL Programming Problems
Author: Xu, Jia
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Prediction,Programming,Natural Language Processing,Databases,Information Technology,Intelligent Tutoring Systems,Difficulty Level,Learning Processes,Computer Science Education,Undergraduate Students,Learning Analytics,Guidelines,Foreign Countries

In an Intelligent Tutoring System (ITS), problem (or question) difficulty is one of the most critical parameters, directly impacting problem design, test paper organization, result analysis, and even the fairness guarantee. However, it is very difficult to evaluate the problem difficulty by organized pre-tests or by expertise, because these solutions are labor-intensive, time-consuming, leakage-prone, or subjective in some way. Thus, it is of importance to automatically evaluate problem difficulty via information technology. To this end, we propose a novel difficulty prediction framework, named SQL-DP, for Structured Query Language (SQL) programming problems, mastering which plays a vital role in learning the database technology. In SQL-DP, semantic features of problem stems and structure features of problem answers in the form of SQL codes are both computed at first, using the NLP and the neural network techniques. Then, these features are used as the input to train a difficulty prediction model with the statistic error rates in tests as the training labels, where the whole modeling does not introduce any experts, some as knowledge labeling. Finally, with the trained model, we can automatically predict the difficulty of each SQL programming problem. Moreover, SQL programming problem answering log data of hundreds of undergraduates from Guangxi University of China are collected, and the experiments conducted on the collected log data demonstrate the propped SQL-DP framework outperforms the state-of-the-art solutions apparently. In particular, SQL-DP decreases the RMSE of difficulty prediction by at most 7.23%, compared with the best-related framework. [For the full proceedings, see ED623995.]
The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues
Author: Tack, Anaïs
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Artificial Intelligence,Dialogs (Language),Bayesian Statistics,Decision Making,Reliability,Models,Teacher Student Relationship,Intelligent Tutoring Systems,Test Construction,Evaluation Methods,Teaching Methods,Comparative Analysis,Computer Software,Task Analysis,Interrater Reliability,Scores,Computer Simulation

How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability. This paper reports on a first attempt at an AI teacher test. We built a solution around the insight that you can run conversational agents in parallel to human teachers in real-world dialogues, simulate how different agents would respond to a student, and compare these counterpart responses in terms of three abilities: speak like a teacher, understand a student, help a student. Our method builds on the reliability of comparative judgments in education and uses a probabilistic model and Bayesian sampling to infer estimates of pedagogical ability. We find that, even though conversational agents (Blender in particular) perform well on conversational uptake, they are quantifiably worse than real teachers on several pedagogical dimensions, especially with regard to helpfulness (Blender: [delta] ability = -0.75; GPT-3: [delta] ability = -0.93). [For the full proceedings, see ED623995.]
Using Machine Learning Explainability Methods to Personalize Interventions for Students
Author: Hur, Paul
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Artificial Intelligence,Individualized Instruction,College Mathematics,Statistics,Learning Strategies,Electronic Learning,Prediction,Student Behavior,Intervention,Undergraduate Students

Machine learning is a powerful method for predicting the outcomes of interactions with educational software, such as the grade a student is likely to receive. However, a predicted outcome alone provides little insight regarding how a student's experience should be personalized based on that outcome. In this paper, we explore a generalizable approach for resolving this issue by personalizing learning using explanations of predictions generated via machine learning explainability methods. We tested the approach in a self-guided, self-paced online learning system for college-level introductory statistics topics that provided personalized interventions for encouraging self-regulated learning behaviors. The system used explanations generated by SHAP (SHapley Additive exPlanations) to recommend specific actions for students to take based on features that most negatively influenced predicted learning outcomes; an &quot;expert system&quot; comparison condition provided recommendations based on predefined rules. A randomized controlled trial of 73 participants (37 expert-system condition, 36 explanation condition) revealed similar learning and topic-choosing behavior between conditions, suggesting that XAI-informed interventions facilitated student statistics learning to a similar degree as expert-system interventions. [For the full proceedings, see ED623995.]
DEI Challenges in the College Admission Counseling Profession
Author: National Association for College Admission Counseling
Date: 2022-03-00
Type: Reports - Research
Subjects: College Admission,Professional Associations,Admissions Counseling,Admissions Officers,Diversity,Inclusion,Equal Education,Student Needs,Counselor Attitudes,Professional Personnel,Counseling Techniques,Counselor Role,Leadership,Racism,Student Characteristics,Counselor Training,Educational Needs,Admission Criteria,College Applicants,Elementary Secondary Education,Outreach Programs

The National Association for College Admission Counseling (NACAC) commissioned this environmental scan of diversity, equity, and inclusion (DEI) practices in college counseling and admission professions in concert with its other recent DEI-related research, training, and initiatives to address the urgent needs of today's students and institutions. Research and policy have explicitly established the benefits of DEI and its crucial importance for realizing ideal educational experiences and outcomes of students, and subsequently, their future prospects in the workforce and society. This environmental scan consisted of a literature review, DEI position scan, and focus groups with college counseling and admission professionals to: (1) assess the current landscape, status, and satisfaction of professionals with DEI in the field; (2) identify DEI practices specific or applicable to college counseling and admission; (3) identify DEI-related challenges and needs for furthering DEI practices and satisfaction, including representation and success of students; and (4) provide recommendations for additional DEI training, staffing, and other school and institutional practices beneficial to college counseling, admission, and student success. The collective findings gathered from relevant literature, a policy and practice scan, and focus groups with college counseling and admission professionals underscore the important role these staff play in increasing and supporting a diverse college student body. Efforts being taken to recruit and train college counseling and admission staff, as well as leadership, with an anti-racist lens should continue to be implemented, assessed, refined, and replicated. [This report is an extension of the &quot;Toward a More Equitable Future for Postsecondary Access&quot; (ED618930) report. This report was co-produced by Salesforce.org.]
Using Neural Network-Based Knowledge Tracing for a Learning System with Unreliable Skill Tags
Author: Karumbaiah, Shamya
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Artificial Intelligence,Middle School Students,Models,Concept Mapping,Mathematics Skills

Considerable amount of research in educational data mining has focused on developing efficient algorithms for Knowledge Tracing (KT). However, in practice, many real-world learning systems used at scale struggle to implement KT capabilities, especially if they weren't originally designed for it. One key challenge is to accurately label existing items with skills, which often turns out to be a herculean task. In this paper, we investigate whether an increasingly popular approach to knowledge tracing, the use of neural network models, can be a partial solution to this problem. We conducted a case study within a commercial math blended learning system. Using the data collected from middle school students' use of the system over two years, we compare the performance of a neural network-based KT model (DKVMN) in three scenarios: 1) with the original (possibly unreliable) system-provided skill tags, 2) with coarser-grained domain tags based on state standards, and 3) without inputting any mappings between content and skills. Our results suggest that including the system-provided skills in the training of the model leads to the worst performance. The best performance is observed when the skills are entirely disregarded. This supports the possibility of bypassing the laborious step of item-skill tagging in real-world learning systems which were not originally designed to work with KT models, especially if the goal is only to predict the performance of a student on future items. We discuss the implications of our findings for practice and future research. [For the full proceedings, see ED623995.]