ethanporcaro/eric-fulltext-meta huggingface dataset:
Applying Psychometric Modeling to Aid Feature Engineering in Predictive Log-Data Analytics: The NAEP EDM Competition
Author: Zehner, Fabian
Date: 2021-00-00
Type: Journal Articles
Subjects: National Competency Tests,Engineering Education,Data Collection,Data Analysis,Competition,Prediction,Response Style (Tests),Psychometrics,Classification,Test Items,Item Response Theory,Elementary School Students,Secondary School Students

The NAEP EDM Competition required participants to predict efficient test-taking behavior based on log data. This paper describes our top-down approach for engineering features by means of psychometric modeling, aiming at machine learning for the predictive classification task. For feature engineering, we employed, among others, the Log-Normal Response Time Model for estimating latent person speed, and the Generalized Partial Credit Model for estimating latent person ability. Additionally, we adopted an &quot;n&quot;-gram feature approach for event sequences. Furthermore, instead of using the provided binary target label, we distinguished inefficient test takers who were going too fast and those who were going too slow for training a multi-label classifier. Our best-performing ensemble classifier comprised three sets of low-dimensional classifiers, dominated by test-taker speed. While our classifier reached moderate performance, relative to the competition leaderboard, our approach makes two important contributions. First, we show how classifiers that contain features engineered through literature-derived domain knowledge can provide meaningful predictions if results can be contextualized to test administrators who wish to intervene or take action. Second, our re-engineering of test scores enabled us to incorporate person ability into the models. However, ability was hardly predictive of efficient behavior, leading to the conclusion that the target label's validity needs to be questioned. Beyond competition-related findings, we furthermore report a state sequence analysis for demonstrating the viability of the employed tools. The latter yielded four different test-taking types that described distinctive differences between test takers, providing relevant implications for assessment practice.
A Descriptive Profile of Online Faculty Training Practices in the Illinois Community College System
Author: Andrews, Melissa
Date: 2021-09-00
Type: Journal Articles
Subjects: Online Courses,College Faculty,Community Colleges,Best Practices,Faculty Development,Course Content,Teaching Methods,Evaluation,Standards,Teacher Competencies,Technological Literacy,Communication Skills,Feedback (Response)

This study investigated the existing training practices for online instructors in the Illinois community college system. As online learning continues to alter the educational landscape, online faculty need to be better prepared to support student success. A survey with questions based on best practices in online instructor training was distributed to the 48 community colleges and campuses in Illinois. The results of the study reveal that orientation and professional development programs are widely offered, yet only a limited number of programs and resources pertain to online pedagogy, teaching strategies, and assessment. There is also a lack of common standards for the format and content of professional training for online instructors. I discussed practical implications to improve professional training for online instructors and thus contribute to online student success.
Conveying Positive Regard in Distance Courses: An Exploratory Study
Author: Harder, Joseph
Date: 2021-09-00
Type: Journal Articles
Subjects: Distance Education,College Students,Student Attitudes,Likert Scales,Factor Analysis,Teacher Student Relationship,Feedback (Response),Student Surveys,Online Courses,Correlation,Social Media,Data Analysis,Test Validity

The present study operationalizes perceived positive regard in the form of a practical measure that can be applied in distance delivery settings. We collected data by surveying distance students at our university. The questions pertained to the quality of learning and the positive regard of the instructor as perceived by the students. Analytical methods included principal components analysis (PCA) to confirm that each group of questions was measuring a common construct, and correlation (Pearson's r) of the constructs. We found that the affect measure was valid and that it was positively correlated with student learning outcomes.
AutoML Feature Engineering for Student Modeling Yields High Accuracy, but Limited Interpretability
Author: Bosch, Nigel
Date: 2021-00-00
Type: Journal Articles
Subjects: Accuracy,Learning Analytics,Models,National Competency Tests,Comparative Analysis,Data Analysis,Hypothesis Testing,Computer Software,Competition,Specialists,Decision Making,Evaluators,Prediction,Time Management,Mathematics Tests,Grade 4,Grade 8

Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The methods we compare, Featuretools and TSFRESH (Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests), have rarely been applied in the context of student interaction log data. Thus, we address research questions regarding the accuracy of models built with AutoML features, how AutoML feature types compare to each other and to expert-engineered features, and how interpretable the features are. Additionally, we developed a novel feature selection method that addresses problems applying AutoML feature engineering in this context, where there were many heterogeneous features (over 4,000) and relatively few students. Our entry to the NAEP competition placed 3rd overall on the final held-out dataset and 1st on the public leaderboard, with a final Cohen's kappa = 0.212 and area under the receiver operating characteristic curve (AUC) = 0.665 when predicting whether students would manage their time effectively on a math assessment. We found that TSFRESH features were significantly more effective than either Featuretools features or expert-engineered features in this context; however, they were also among the most difficult features to interpret based on a survey of six experts' judgments. Finally, we discuss the tradeoffs between effort and interpretability that arise in AutoML-based student modeling.
Fostering Student Awareness of Team Skills: A Participative Team Formation Process for Class Projects
Author: Berka, Gregory C.
Date: 2021-00-00
Type: Journal Articles
Subjects: Cooperative Learning,Teamwork,Class Activities,Student Projects,Grouping (Instructional Purposes),Instructional Effectiveness,College Students

This essay outlines a participative team formation process for class projects with resources to support instructors in implementing this process. This hybrid process, integrating self-selection and teacher assigned methods, includes four touch points that foster students' awareness of effective team behaviors and the presence (or absence) of these behaviors within themselves and in team members. The awareness can provide students the foundation for developing team skills--beneficial in both team projects and in organizational teams.
Modeling NAEP Test-Taking Behavior Using Educational Process Analysis
Author: Patel, Nirmal
Date: 2021-00-00
Type: Journal Articles
Subjects: Learning Analytics,National Competency Tests,Evaluation Methods,Prediction,Student Behavior,Testing,Computer Assisted Testing,Models,Competition,Markov Processes,Mathematics Tests,Test Items,Item Analysis

Process Analysis is an emerging approach to discover meaningful knowledge from temporal educational data. The study presented in this paper shows how we used Process Analysis methods on the National Assessment of Educational Progress (NAEP) test data for modeling and predicting student test-taking behavior. Our process-oriented data exploration gave us insightful findings of how students were interacting with the digital assessment system over time. To discover what processes students were following during the NAEP Digital Assessment, we first developed an innovative set of research questions. Then, we used Process Analysis methods to answer these questions and created a set of features that described student behavior over time. These features were used to create an ensemble model that aimed to accurately predict the digital test-taking efficiency of the students taking NAEP. Our model emerged as one of the most successful models in the 2019 NAEP Data Mining Competition, scoring second place out of 89 teams.
Effective Practices in Facilitating Nontraditional Adult Learners&apos; Dissertation Writing in an Online Doctoral Program
Author: Jiang, Mei
Date: 2021-09-00
Type: Journal Articles
Subjects: Nontraditional Students,Adult Students,Doctoral Students,Doctoral Dissertations,Competency Based Education,Mentors,Communities of Practice,Electronic Learning

To address the challenges nontraditional adult doctoral learners face in completing dissertations online, we reflect on the effective practices utilized in an online doctoral-level educational leadership program at a public university in the United States. This paper focused on two major topics: 1) how to prepare students to write a dissertation through coursework based on the Competency-based Education (CBE) theory; and 2) how to build relationships between mentors and students writing dissertations based on the Community of Inquiry (CoI) framework. The theoretically founded practices based on CBE and CoI must be utilized in building an interactive, collaborative online learning community.
Process Mining Combined with Expert Feature Engineering to Predict Efficient Use of Time on High-Stakes Assessments
Author: Levin, Nathan A.
Date: 2021-00-00
Type: Journal Articles
Subjects: Learning Analytics,Data Collection,Competition,Prediction,Time Factors (Learning),High Stakes Tests,Middle School Students,Grade 8,Mathematics,Computer Assisted Testing,Classification,Efficiency,Scores,Computation,Engineering,Models

The Big Data for Education Spoke of the NSF Northeast Big Data Innovation Hub and ETS co-sponsored an educational data mining competition in which contestants were asked to predict efficient time use on the NAEP 8th grade mathematics computer-based assessment, based on the log file of a student's actions on a prior portion of the assessment. In this work, a combined approach of process mining and expert feature engineering was used to build a large set of features that were then trained with an Extreme Gradient Boosting machine learning model to classify students based on whether they would use their time efficiently. Predictions were evaluated throughout the competition on half of a hidden data set and then the final results were based on the second half of the hidden data set. The approach used here earned the top score in the competition. The work presented elaborates on the combined technique for analyzing computer-based assessment log-file data with the hope that this approach will offer valuable insights for future predictive model building in educational data mining.
Exploring Value Variations in Instructor Presence Techniques for Online Students
Author: Robertson, Sarah N.
Date: 2021-00-00
Type: Journal Articles
Subjects: Undergraduate Students,College Faculty,Teacher Student Relationship,Online Courses,Interpersonal Communication,Feedback (Response),Student Interests,Peer Relationship,Course Content,Student Attitudes,Value Judgment

This study sought to define and measure online undergraduate students' perceived value of instructor presence techniques across five communication mediums per pedagogical goal (connection to course content, connection to classmates, connection to the instructor, foster interest, and facilitate immediate feedback). Students found personalized written messages from an instructor (M=4.61) as most valuable due to their ability to provide immediate feedback. Interactive phone calls (M=3.24) were the least valuable in the area of familiarity. Results indicate all instructor presence techniques had value, but some were more valuable than others.
Collaborative Autoethnography: Best Practices for Developing Group Projects
Author: Hornsby, Elizabeth Robertson
Date: 2021-00-00
Type: Journal Articles
Subjects: Cooperative Learning,Ethnography,Group Activities,Student Projects,Best Practices,Qualitative Research,Research Methodology,Group Dynamics,Curriculum Design

Collaborative Autoethnography (CAE) is an emerging practice that combines group interaction with qualitative research. Group projects are often deployed in course design to maximize the value of collaborative learning environments. Using existing scholarship, we describe best practices for group projects that apply principles of CAE. To advance the premise of the paper beyond descriptive summaries of pedagogical inquiry, we utilize a best practices mechanism to present a coherent guide for project collaborators to use in various classroom settings. The best practices proposed are research validated by existing CAE and project management literature.
Effect of Student Satisfaction on Elearning Quality and Learning Outcome among Malaysian Undergraduate Nursing Students
Author: Ching, Chang Woan
Date: 2021-09-00
Type: Journal Articles
Subjects: Foreign Countries,Student Satisfaction,Undergraduate Students,Nursing Education,Educational Quality,Electronic Learning,Educational Technology,Outcomes of Education,Success

Elearning has become an important and ubiquitous instructional tool across a broad range of programs in institutions of higher education. This is evident today in nursing education where elearning in a blended approach provides both enormous and flexible opportunities for working nurses to further their education and to engage in continuous professional development for life-long learning. We conducted a pilot study to test the feasibility of participant recruitment, data collection, and online survey in an elearning education program in Malaysia. The conceptual framework developed for the study was based on DeLone and McLean (2003) Information Systems Success Model to examine any mediation effect of student satisfaction on elearning quality and learning outcome among nursing undergraduates in a local elearning program. We used the Partial Least Squares approach to analyze the possible effects on the relationships among the variables studied. The exploration process to determine the feasibility of the preliminary online survey helping to contribute to the main study to be conducted in an elearning nursing context. The findings revealed that there were significant relationships between the dimensions of system quality and service quality with student satisfaction and learning outcome. The results showed that there was a mediating effect of student satisfaction on the relationship between elearning quality and learning outcome. The study emphasizes the importance of an initial understanding of the learning environmental needs of learners to provide a credible and meaningful learning experience for working nurses in elearning nursing programs.
How Online Business School Instructors Address Academic Integrity Violations
Author: Nelson, Darrin
Date: 2021-09-00
Type: Journal Articles
Subjects: Integrity,Ethics,Cheating,Plagiarism,College Faculty,Teacher Attitudes,Teacher Responsibility,Business Schools,Classroom Techniques,Prevention,Electronic Learning,Online Courses,Time Management,Accountability

While online learning has dramatically increased over the past few years, so have opportunities for students to cheat, plagiarize, or commit other academic integrity violations in the online learning environment. Some online instructors effectively address these issues, while others do not. The purpose of this qualitative case study was to explore how online business school instructors address academic integrity violations. To examine in what manner academic integrity issues were being addressed in the online classroom, 12 teachers at a university located in Florida were interviewed to obtain their perspective and instructional practices regarding academic integrity online. Results indicate all participants believe it is their job to prevent academic integrity violations; however, only two instructors have reported it through school administrators, with many participants deciding to address the issue in their own classroom and not report it outside to school leadership for help addressing the issue. The importance of this study shows that most online instructors are doing what they believe is their best to address integrity violations; however, they may be promoting it by not reporting it outside of the classroom to school administration.
Necessity and Obligation Modals in English Academic Discourse: A Corpus-Based Analysis
Author: Oktavianti, Ikmi Nur
Date: 2019-00-00
Type: Journal Articles
Subjects: English for Academic Purposes,Second Language Learning,Second Language Instruction,North American English,Computational Linguistics,Verbs,Word Frequency,Language Usage

Modals are linguistic units that seem to be ubiquitous in nearly all genres and text categories. However, there are some tendencies in which some modals are more likely to occur in a particular text category than the others. It is said that modals are less frequent in academic texts compared to fiction and news. This paper then aims at describing the modals, focusing to those expressing necessity/obligation, by using corpus-based analysis. This study uses a general reference corpus, Corpus of Contemporary American English, and compiled the data from the academic subcorpus. The results show that statistically the usage frequency of necessity and obligation modals is negligible; however, no matter how small it is, it still purports to mark something. Among the modals in the same category there are some tendencies, e.g. modal should is the most frequent of all, followed by must, have to, shall, be supposed to, and have got to. The collocate analysis focuses on should and must and found out that the most frequent verb type following these two modals belong to thinking verbs (e.g., consider, learn, understand). However the most frequent modal constructions are should have and must have enabling a slightly different interpretation. Besides, due to the nature of language in an academic setting, it is thus predictable that the use of the core modals (e.g., should, must) is more frequent than the quasi-modals (e.g., have to).
Instrument Development for Investigating Students&apos; Intention to Participate in Online Discussion Forums: Cross-Cultural and Context Adaptation Using SEM
Author: Hasani, Lintang Matahari
Date: 2021-09-00
Type: Journal Articles
Subjects: Test Construction,Intention,Student Participation,Online Courses,Discussion,Cooperative Learning,Structural Equation Models,Test Reliability,Test Validity

There is a need to investigate student intention to participate in online discussion forums in the context of online collaborative learning due to the importance of student participation and the widespread student inactivity reported in some studies. Student participation in online discussion forums could be predicted by the constructs included in the Theory of Planned Behavior (TPB). This paper presents a cross-cultural adaptation (CCA) and validation of a questionnaire based on TPB for analysis using Structural Equation Modelling (SEM). The CCA was conducted to adapt the English items included in the questionnaire for use in an Indonesian context. This is part of our study on factors that affect student intention to participate in online discussion forums. The rationale for conducting a CCA was the need to cater to the different characteristics of the language and the cultural backgrounds of the participants of this study in comparison to the participants involved in the construction of the original measurement items. The adaptation and the validation of the questionnaire involved a literature review, back-translation, an expert review, pretesting, and a statistical validation test to ensure its reliability and validity. The result shows that the adapted instrument is valid and reliable to be used for further SEM analysis (i.e., structural model evaluation). Moreover, this paper demonstrates that expert-reviewed adaptation and the back-translation method could produce valid and reliable measurement items for SEM. Therefore, future studies that utilize SEM with cross-cultural adapted items from relevant prior studies should consider adopting expert-reviewed adaptation and back-translation methods.
Practicing Genre-Based Language Teaching Model to Improve Students&apos; Achievement of Writing Skills
Author: Haerazi
Date: 2019-00-00
Type: Journal Articles
Subjects: Second Language Learning,Second Language Instruction,Teaching Methods,Writing Skills,Language Styles,English (Second Language),Action Research,Writing Achievement,Writing Instruction,Grades (Scholastic),Writing Processes,Undergraduate Students,Foreign Countries

This study investigated the use of genre-based language teaching model in the teaching of writing skills to improve students' low achievement. It was aimed at improving the achievement of the fifth semester of English language education study program. This study was classroom action research. The subjects were in the 4th semester in academic year 2017/2018. The procedure of the study included two steps namely the preliminary study and the action. The preliminary study collected information dealing with the students' problems and the action presented the steps of the classroom action research. The data were collected through observation, questionnaire, and testing. The data were analyzed quantitatively and qualitatively. The result of the study showed that the genre-based language teaching model can improve the students' writing achievement. Before the action, the average score of students' writing achievement was 55 and the average score after the action was 76. The result was higher than the subject minimum passing grade which is 75. The improvement was supported by two indicators. First, students felt it was easy to write a descriptive text because the teaching steps guided them to practice writing much more according to the generic structures of the text. Last but not least, students felt compatible with the topics asked to be written about because they have background knowledge of them.
The Influence of Instructional Syntax to Engage EFL Students in Online Learning Environment
Author: Darmawansah
Date: 2019-00-00
Type: Journal Articles
Subjects: Instructional Effectiveness,Second Language Instruction,Second Language Learning,English (Second Language),Learner Engagement,Interaction,Syntax,Lesson Plans,Language Teachers,Active Learning,Inquiry,Foreign Countries

This paper aims to analyse the instructional syntax for an on line learning environment in English language learning. The data collection used lesson plans in a Spain-based English Academy. The research method used Hemphill's engagement criteria to find the syntax characteristic in the lesson plans instructions. Also, the study analysed the factors of engagement during the learning session. The study revealed the frequencies of the instructional syntax used by EFL teachers and how these instructions engage the students. The lessons plans were coded to find the most syntax used and the engagement criteria. The result identified 'ask' is the most used syntax with 15.3% while inquiries method (38.9%) was using the most in teaching students online. Moreover, the distribution item of engagement factors as EFL online teachers' focus group discussion shows how the engagement model can be used for English learners. This study will contribute as an expanding method of English language learning in an online setting widely.