ethanporcaro/eric-fulltext-meta huggingface dataset:
Stacked Ensemble Learning for Propensity Score Methods in Observational Studies
Author: Autenrieth, Maximilian
Date: 2021-00-00
Type: Journal Articles
Subjects: Probability,Artificial Intelligence,Educational Research,Statistical Bias,Observation,Computation,College Students,Outcomes of Education

Propensity score methods account for selection bias in observational studies. However, the consistency of the propensity score estimators strongly depends on a correct specification of the propensity score model. Logistic regression and, with increasing popularity, machine learning tools are used to estimate propensity scores. We introduce a stacked generalization ensemble learning approach to improve propensity score estimation by fitting a meta learner on the predictions of a suitable set of diverse base learners. We perform a comprehensive Monte Carlo simulation study, implementing a broad range of scenarios that mimic characteristics of typical data sets in educational studies. The population average treatment effect is estimated using the propensity score in Inverse Probability of Treatment Weighting. Our proposed stacked ensembles, especially using gradient boosting machines as a meta learner trained on a set of 12 base learner predictions, led to superior reduction of bias compared to the current state-of-the-art in propensity score estimation. Further, our simulations imply that commonly used balance measures (averaged standardized absolute mean differences) might be misleading as propensity score model selection criteria. We apply our proposed model -- which we call GBM-Stack -- to assess the population average treatment effect of a Supplemental Instruction (SI) program in an introductory psychology (PSY 101) course at San Diego State University. Our analysis provides evidence that moving the whole population to SI attendance would on average lead to 1.69 times higher odds to pass the PSY 101 class compared to not offering SI, with a 95% bootstrap confidence interval of (1.31, 2.20).
Measurement Scale Development: Researcherly Disposition in Teacher Education in Turkey
Author: Yokus, Gürol
Date: 2021-04-00
Type: Journal Articles
Subjects: Measures (Individuals),Test Construction,Test Validity,Test Reliability,Teacher Researchers,Preservice Teachers,Undergraduate Students,Value Judgment,Research Skills,Student Characteristics,Public Colleges,Preservice Teacher Education,Teacher Competencies,Foreign Countries

Despite being an important dimension of teachers' professional development, researcherly disposition is not sufficiently emphasized in initial teacher education. Researcherly disposition can be explained as intentional affective, cognitive and operational tendency to follow existing research in specific contexts, engaging in research production and sharing research, in order to strengthen the link between learning, research and teaching and to direct instruction. This research aims to develop an instrument for identifying researcherly disposition in teacher education context. For the construct validity, exploratory factor analysis was conducted with data collected from 350 pre-service teachers and confirmatory factor analysis with another study group of 325 pre-service teachers. Then, final scale was used with 338 participants. After the model was verified, the Researcherly Disposition Scale was developed as 18-item four-factor scale. Cronbach Alpha coefficient correlation of scale was found out to be ([alpha])=0.88 and the total explained variance appeared to be %55.60. This four-factor structure indicated that the Researcherly Disposition Scale was valid and reliable for future studies. Additionally, female pre-service teachers in Turkey have higher researcherly disposition. Those who participated two or more congress have higher researcherly dispositions compared to those who participated to one or none. Also, those who follow one or more journals have higher researcherly dispositions compared to those who do not follow any journal. Researcherly disposition differs by departments of pre-service teachers. For recommendation, pre-service teachers should meet up with research-oriented professionals who elaborate on teaching and they should be encouraged to follow educational research journals and academic congresses in initial teacher education.
Affect, Support, and Personal Factors: Multimodal Causal Models of One-on-One Coaching
Author: Chen, Lujie Karen
Date: 2021-00-00
Type: Journal Articles
Subjects: Causal Models,Coaching (Performance),Statistical Analysis,Correlation,Statistical Inference,Learning Analytics,Elementary School Students,Parent Student Relationship,Problem Solving,Mathematics

Human one-on-one coaching involves complex multimodal interactions. Successful coaching requires teachers to closely monitor students' cognitive-affective states and provide support of optimal type, timing, and amount. However, most of the existing human tutoring studies focus primarily on verbal interactions and have yet to incorporate the rich aspects of multimodal cognitive-affective experiences. Meanwhile, the research community lacks principled methods to fully exploit complex multimodal data to uncover the causal relationships between coaching supports, students' cognitive-affective experiences, and their stable individual factors. We explore an analytical framework that is explainable and amenable to incorporating domain knowledge. The proposed framework combines statistical approaches in Sparse Multiple Canonical Correlation, causal discovery, and inference methods for observations. We demonstrate this framework using a multimodal one-on-one math problem-solving coaching dataset collected in naturalistic home environments involving parents and young children. The insights derived from our analyses may inform the design of effective technology-inspired interventions that are personalized and adaptive.
What&apos;s Next? Sequence Length and Impossible Loops in State Transition Measurement
Author: Bosch, Nigel
Date: 2021-00-00
Type: Journal Articles
Subjects: Sequential Approach,Probability,Measurement Techniques,Statistical Analysis,Time,Computer Assisted Instruction,College Students,Foreign Countries

Transition metrics, which quantify the propensity for one event to follow another, are often utilized to study sequential patterns of behaviors, emotions, actions, and other states. However, little is known about the conditions in which application of transition metrics is appropriate. We report on two experiments in which we simulated sequences of states to explore the properties of common transition metrics (conditional probability, D'Mello's &quot;L,&quot; lag sequential analysis, and Yule's &quot;Q&quot;) where results should be null (i.e., random sequences). In experiment 1, we found that transition metrics produced statistically significant results with non-null effect sizes (e.g., &quot;Q&quot; &gt; 0.2) when sequences of states were short. In experiment 2, we explored situations where consecutively repeated states (i.e., loops, or self-transitions) are impossible -- e.g., in digital learning environments where actions such as hint requests cannot be made twice in a row. We found that impossible loops affected all transition metrics (e.g., &quot;Q&quot; = 0.646). Based on simulations, we recommend sequences of length 50 or more for transition metric analyses. Our software for calculating transition metrics and running simulated experiments is publicly available.
Extending Adaptive Spacing Heuristics to Multi-Skill Items
Author: Choffin, Benoît
Date: 2021-00-00
Type: Journal Articles
Subjects: Heuristics,Time Factors (Learning),Memorization,Time Management,Repetition,Skill Development,Selection,Artificial Intelligence,Computer Uses in Education,Mastery Learning

Adaptive spacing algorithms are powerful tools for helping learners manage their study time efficiently. By personalizing the temporal distribution of retrieval practice of a given piece of knowledge, they improve learners' long-term memory retention compared to fixed review schedules. However, such algorithms are generally designed for the pure memorization of single items, such as vocabulary words. Yet, the spacing effect has been shown to extend to more complex knowledge, such as the practice of mathematical skills. In this article, we extend three adaptive spacing heuristics from the literature for selecting the best skill to review at any timestamp given a student's past study history. In real-world educational settings, items generally involve multiple skills at the same time. Thus, we also propose a multi-skill version for two of these heuristics: instead of selecting one single skill, they select with a greedy procedure the most promising subset of skills to review. To compare these five heuristics, we develop a synthetic experimental framework that simulates student learning and forgetting trajectories with a student model. We run multiple synthetic experiments on large cohorts of 500 simulated students and publicly release the code for these experiments. Our results highlight the strengths and weaknesses of each heuristic in terms of performance, robustness, and complexity. Finally, we find evidence that selecting the best subset of skills yields better retention compared to selecting the single best skill to review.
Mapping Python Programs to Vectors Using Recursive Neural Encodings
Author: Paassen, Benjamin
Date: 2021-00-00
Type: Journal Articles
Subjects: Data Analysis,Programming Languages,Networks,Novices,Prediction,Decision Making,Computer Software,Learning Analytics,Computer Science Education,Higher Education

Educational data mining involves the application of data mining techniques to student activity. However, in the context of computer programming, many data mining techniques can not be applied because they require vector-shaped input, whereas computer programs have the form of syntax trees. In this paper, we present ast2vec, a neural network that maps Python syntax trees to vectors and back, thereby enabling about a hundred data mining techniques that were previously not applicable. Ast2vec has been trained on almost half a million programs of novice programmers and is designed to be applied across learning tasks &quot;without re-training,&quot; meaning that users can apply it without any need for deep learning. We demonstrate the generality of ast2vec in three settings. First, we provide example analyses using ast2vec on a classroom-sized dataset, involving two novel techniques, namely progress-variance projection for visualization and a dynamical systems analysis for prediction. In these examples, we also explain how ast2vec can be utilized for educational decisions. Second, we consider the ability of ast2vec to recover the original syntax tree from its vector representation on the training data and two other large-scale programming datasets. Finally, we evaluate the predictive capability of a linear dynamical system on top of ast2vec, obtaining similar results to techniques that work directly on syntax trees while being much faster (constant- instead of linear-time processing). We hope ast2vec can augment the educational data mining toolkit by making analyses of computer programs easier, richer, and more efficient.
The Community of Bilingual English-Spanish Speakers Exploring Issues in Science and Health: Experiences during the COVID-19 Pandemic
Author: Lucero, Julie E.
Date: 2021-10-00
Type: Journal Articles
Subjects: Student Diversity,Bilingual Students,Peer Teaching,Mentors,STEM Education,Vocational Interests,High School Students,COVID-19,Pandemics,School Closing,Summer Programs,Educational Technology,Electronic Learning,Allied Health Occupations Education,Spanish,English,Career Exploration,Leadership Training,Student Research,Parent Participation,Online Courses,Hispanic American Students

Language diversity is increasing in the United States. This growth has implications for language preference, cost, quality, and client outcomes in health services settings. However, language diversity among medical and allied health professionals is lacking. Education pipeline programs are a mechanism to prepare bi- and multi-lingual diverse students to enter health careers. The Community of Bilingual English-Spanish Speakers Exploring Issues in Science and Health (CBESS) is one such program. Through peer mentorship from Leadership Trainees (LT), and a multicomponent 17-month education curriculum, CBESS was designed to increase interest in STEM careers among English-Spanish bilingual high school youth. In 2020, the COVID-19 pandemic interrupted high school students' education and forced programs to innovate. CBESS was no exception. The most significant modifications were to a) expectations of SRs for a successful Summer Virtual Research Program (SVRP), b) LT roles, and c) scope and delivery of summer science content. A preliminary evaluation was conducted from data collected through pre-post surveys, process data, and focus groups. Among the outcomes were a significant increase in science knowledge among SVRP youth participants as well as no significant differences between cohort 1 and 2 suggesting that changes did not impede program goals. LTs highlighted skills needed and role of mentors. Adaptations were successful and will continue with the 2021 cohort. [Note: The issue number (3) shown on the first page of the PDF is incorrect. The correct issue number for this article is 4.]
Targeted, Sustainable, Adept: Transforming Jackson State University STEM PD Delivery for Mississippi 6-12th Grade Teachers during COVID-19
Author: Kirtman, Nisaa
Date: 2021-10-00
Type: Journal Articles
Subjects: Program Development,STEM Education,Secondary School Teachers,COVID-19,Pandemics,Educational Change,Faculty Development,Online Courses,Self Efficacy,Web Based Instruction,Teacher Attitudes

Since 2017, Jackson State University's (JSU) Minority Advancement through Recruitment and Retention in Science via Outreach Program (MARRS-OP) developed a series of professional development (PD) programs designed for 6th-12th science teachers in under-resourced Mississippi school districts. Due to COVID-19, MARRS-OP's original program design was modified to implement innovative online PD courses, designed to expand and sustain the reach of STEM teaching and learning opportunities -- particularly for lab experiments. The PD topics included science-subject-based courses centered around equity, science, and technology. Tools for assessing MARRS-OP outcomes included a remote readiness screener for data collection, pre- and post-program survey data collection, and teacher presentation reviews. Despite an array of challenges, teachers reported statistically significant increases in confidence in their ability to teach biology and physics. Teachers also saw value in remote PD, as it provided the ability to experience virtual instruction from their students' perspective, and the opportunity to collaborate with other teachers and expand their network. This article shows that by preparing science teachers to customize approaches for their students, and maintain flexibility, that initiatives such as MARRS-OP can serve as an empowering resource for teachers despite the accessibility and engagement challenges encountered by the sudden shift to online pedagogy.
The Health Sciences and Technology Academy: Re-Imagining Programmatic Delivery during the COVID-19 Pandemic
Author: McKendall, Sherron Benson
Date: 2021-10-00
Type: Journal Articles
Subjects: After School Programs,STEM Education,High School Students,COVID-19,Pandemics,Summer Programs,Educational Change,Distance Education,Student Attitudes,Learner Engagement,Curriculum Design,Electronic Learning,Student Projects,Student Research,Workshops,Service Learning

Out-of-school time academic/STEM programs provide educational enrichment to a myriad of student populations with some designed to assist those underrepresented and at-risk who desire to purse post-secondary studies. One such program in West Virginia (WV) is the Health Sciences and Technology Academy (HSTA), which provides hands-on, in person and engaging educational enrichment with the intent to increase the college going rates of Appalachia's most vulnerable youth. In March 2020, HSTA key personnel encountered the task of redesigning program delivery due to the immediate shutdown of all in person operations resulting from the novel coronavirus, SARS-CoV-19 (COVID-19) pandemic. This paper discusses the programmatic changes of program delivery during HSTA's key program components--the Summer Institute and the Community Based Programming. This paper also utilizes repeated measures one-way multivariate analysis (MANOVA) and paired t-test analysis to examine participants' perspectives on learning, enjoyment and satisfaction of the aforementioned components prior to and during the COVID-19 pandemic.
Beyond Skill: Students&apos; Dispositions towards Math
Author: Maciejewski, Wesley
Date: 2021-00-00
Type: Journal Articles
Subjects: Student Characteristics,Student Attitudes,Developmental Studies Programs,College Mathematics,Mathematics Instruction,College Freshmen,Predictor Variables,Relevance (Education),Self Esteem,Mathematics Skills,Student Interests,Academic Persistence,Mathematics Anxiety,Problem Solving

Universities have been forced to confront the increasing placement of students into developmental courses and the subsequent lack of success students encounter. In an effort to better understand how students' attitudes and dispositions towards mathematics impact this trend, we explored the differences in nine characteristics between students in developmental and college-level mathematics courses from a sample of 676 freshmen at a large, public university. Findings showed that students in developmental math did have significantly less-favorable dispositions towards mathematics. However, a cluster analysis revealed that the students were not as homogeneous as may first have appeared, showing a mix of favorable and less-favorable attitudes and dispositions. Further, a regression analysis revealed three student characteristics significantly predicted students' placement into developmental courses: their perspective on the applicability of math to the world around them, low confidence, and a limited view of what constitutes valid mathematical solutions. Taken together, these results challenge traditional, skill-based assessments of postsecondary mathematics preparedness.
Unknown Territory: K-12 STEM Summer Exploration through Zoom
Author: Roberts, Kari
Date: 2021-10-00
Type: Journal Articles
Subjects: Elementary Secondary Education,Summer Programs,Videoconferencing,Distance Education,STEM Education,Program Effectiveness,Middle School Students,Learning Activities,Synchronous Communication,Asynchronous Communication,Learner Engagement

When COVID-19 shut down in-person programs across the world in 2020, we (the National High Magnetic Field Laboratory (MagLab)) as practitioners were in unknown territory. This paper outlines the structure of a 10-week online Summer Exploration Series (SES) program along with the evaluation performed by staff at the MagLab in 2020. The goal of the SES program was to increase youth's interest in STEM and knowledge of STEM careers relevant to materials science. Each week included live and asynchronous components. All of the participants (n=86) rated the program as above average or higher, crediting the program with teaching them about new STEM careers/topics and increasing their interest in STEM. Our evaluation indicated that the live sessions, particularly those that offered more opportunities for interaction, were rated higher than the asynchronous sessions, providing evidence to the benefits of live - even if online -- sessions on increasing interest in STEM. The lessons learned through the program can inform other organizations as we continue into the new normal.
Study Plans Promote Daily Review
Author: Diehl, Tara
Date: 2021-00-00
Type: Journal Articles
Subjects: Study Habits,Study Skills,Skill Development,Scheduling,Mathematics Achievement,Developmental Studies Programs,Remedial Mathematics,College Students,Algebra,Grades (Scholastic),Review (Reexamination),Public Colleges

This paper analyzes how weekly study plans can increase student achievement in developmental mathematics courses at a four-year public institution of higher education. Quantitative data were collected regarding student completion of weekly study plans through an experimental methodology. Results show that students in the experimental group earned higher average grades across all categories (each exam and overall course grade) when compared to the control group. A discussion of findings and limitations is included.
Instruction Type, Student Major, and Success in Developmental Mathematics
Author: Coltharp, Jean
Date: 2020-00-00
Type: Journal Articles
Subjects: Developmental Studies Programs,Remedial Mathematics,College Students,Majors (Students),Online Courses,Conventional Instruction,Grades (Scholastic),Teaching Methods,Withdrawal (Education)

The low success rate of developmental (or remedial) mathematics courses has been a great concern at the higher education level. In this research, student success is considered in terms of final grade, D/F percentage, and withdrawal percentage. The study examines student success at a Midwestern university as it relates to instructional delivery type (online vs traditional) and student major (arts and humanities, STEM, and undeclared). It was found that final grade and D/F percentage were statistically significant, whereas withdrawal percentage was not when the data were analyzed with a two-way analysis of variance.
Supporting Success: Learning Assistance for Adult-Arrival Immigrant Students
Author: Suh, Emily K.
Date: 2020-00-00
Type: Journal Articles
Subjects: Immigrants,Student Needs,Postsecondary Education,Academic Support Services,Developmental Studies Programs,Student Motivation,School Personnel,Role,Cultural Awareness,Knowledge Level,Bilingual Students,English Language Learners,Adult Students,Urban Schools,Two Year Colleges,Help Seeking,Expectation

Generation 1 learners are multilingual, adult-arrival, immigrant students who begin their U.S. education in adult ESL. With their nontraditional academic backgrounds, these learners often require support when beginning postsecondary education; however, postsecondary learning assistance professionals may not understand these learners' unique needs and strengths. This multiple case study explores five learners' experiences accessing learning support during their first term in developmental education courses. Findings indicate that, although the learners were highly motivated to seek help, they remained confused over the roles and expectations of learning support professionals. Furthermore, support staff required additional knowledge and cultural competence to assist these students.