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Although various academic disciplines use data papers to support effective research practices, data papers are still uncommon in the educational sciences. Main goals of data papers are enhancing transparency regarding research processes and supporting data sharing among researchers and thus, open science. As many educational research projects include personal data often of minors, publishing raw data can be challenging due to privacy regulations and laws (GDPR). The present article aims at exploring how a data paper can contribute to open science and enhancing transparency regarding research and publication processes for educational research projects, often containing personal data that cannot be made openly available in its raw form. To this end, we describe a dataset of a research project on teachers' noticing as basis for their classroom management. As this project includes rich process-tracing recording methods, the dataset is diverse and serves as basis for multiple analyses and publications while containing personal data of teachers and minors. By elaborating on the characteristics of the dataset, its gathering, analysis approaches and sharing preprocessed and anonymized data files, this data paper explores how to contribute to transparency and open science in educational sciences while acting within the boundaries set by privacy regulations and laws.

The five papers published by Hodgkin and Huxley in 1952 are seminal works in the field of physiology, earning their authors the Nobel Prize in 1963 and ushering in the era of membrane biophysics. The papers present a considerable challenge to the novice student, but this has been partly allayed by recent publications that have updated the reporting of current and voltage to reflect the modern convention and two books that describe the contents of the papers in detail. A disadvantage is that these guides contain hundreds of pages, requiring considerable time and energy on behalf of the reader. We present a concise guide to the Hodgkin and Huxley papers that includes only essential content, with the data presented in a linear and logical manner. We have color-coded figures for ease of understanding and included boxes that summarize key information for easy reference. It is our expectation that this article will act as an accessible introduction for students to the work of Hodgkin and Huxley and hopefully foster an appreciation for a fascinating story that repays in-depth study.

It was aimed in this study to reveal the general tendency of studies in the field of education by examining the papers in the high-impact A-class SSCI journals, to which qualified papers are accepted from all around the world, in terms of their dependent-independent variables, sample or study groups, research designs, data collection instruments, and data analysis techniques. The descriptive survey model was used in the research. The population of the research was all the journals surveyed in the field of educational sciences by SSCI. The journals and papers examined were selected with the purposive sampling method. 169 papers from six journals were subjected to examination within the scope of the research. Descriptive analysis and content analysis methods were used for analyzing the data. It was consequently seen that the papers used dependent variable of "student" the most which was followed by "teacher." The most studied variable along with "student" was "academic performance." It was found that a quite large number of dependent variables were used in the papers examined. It can be understood that studies on students among all study groups occupied the largest place, which was followed by teachers. It was seen that quantitative data analyses and experimental research studies was addressed more in the papers. Several documents and tests were mostly preferred as data collection instruments. It was noticed that the most used data analysis method was the regression analysis. Finally, some recommendations were developed in accordance with the research results.

This paper takes a look backward while simultaneously looking to the future for MIS departments that are making the transition to Analytics departments. MIS has a long past of providing a base of skills supporting organizations. We examine this history as well as how the blending of MIS with business translator and modeling skills has led to the development of analytics programs and concentrations. While the transition to analytics has taken place in many MIS departments at least partially, the question is how long analytics will remain a focus and when will the next major shift occur.

Introduction: Paper folding and punched hole tests are used to measure spatial abilities in humans. These abilities are relevant since they are associated with success in STEM (Science, Technology, Engineering, and Mathematics). This study addresses the challenge of teaching spatial reasoning skills using an educational videogame, the Paper Folding Reasoning Game. Methods: The Paper Folding Reasoning Game is an interactive game which presents activities intended to help users train and understand how to fold a paper to get a specific shape (Part I) and the consequence of punching a hole on a folded paper (Part II). This educational videogame can automatically generate paper-folding-and-punched-hole questions with varying degrees of difficulty depending on the number of folds and holes made, thus producing additional levels for training due to its embedded reasoning mechanisms (Part III). Results: This manuscript presents the results of analyzing the gameplay data gathered by the Paper Folding Reasoning Game in its three parts. For Parts I and II, the data provided by 225 anonymous unique players are analyzed. For Part III (Mastermode), the data obtained from 894 gameplays by 311 anonymous unique players are analyzed. In our analysis, we found out a significant difference in performance regarding the players who trained (i.e., played Parts I and II) before playing the Mastermode (Part III) vs. the group of players who did not train. We also found a significant difference in players' performance who used the visual help (i.e., re-watch the animated sequence of paper folds) vs. the group of players who did not use it, confirming the effectiveness of the Paper Folding Reasoning Game to train paper-folding-and-punched-hole reasoning skills. Statistically significant gender differences in performance were also found. [ABSTRACT FROM AUTHOR]

The apps for drawing are present in our children's life. Nevertheless, little is known about the impact of mobile technology on the freehand drawing educational activity. There are few works which are contextualised within short periods of time, with teachers who are not theirs and, in some cases, outside the children's classroom. In this paper, we are focussed on the use of technology on freehand drawing activity. Thus, we have compared the graphics produced by 4- and 5-year-old children with paper&crayon in comparison with those with tablet-finger. Children made the drawings during a planned free-drawing activity, in their ordinary classrooms, with their teachers and during five sessions. Assessment of drawings has evidenced tablet feasibility for making graphics. Nevertheless, with the passing of time, quality of graphics (tablet-finger vs paper&crayons), are nearly matched, demonstrating the low impact level technology has on this activity. In addition, if drawings are analysed specifically according to ages, results have shown that both groups have to develop adaptation strategies of visual perceptual skills and fine motor skills for the touch screen in order to obtain the same quality in the drawings made on both support types. [ABSTRACT FROM AUTHOR]

This paper provides a descriptive content analysis of biology education research papers published in eight major academic journals indexed in Social Science Citation Index [SSCI] of Thomson Reuters® from 1997 to 2014. Total of 1376 biology education research [BER] papers were examined. The findings indicated that most of the papers were published in the JBE and IJSE, and frequently studied topics were environment and ecology, genetics and biotechnology, and animal form and function. The findings were also indicated that learning, teaching and attitudes was in the forefront as the frequently investigated subject matters, undergraduate and secondary school students were mostly preferred as sample group and sample size mostly varies between 31-100 and 101-300. In addition, it was found out that interactive qualitative research designs were mostly preferred. Besides, that single data collection tool was generally used and this data collection tool included questionnaires, interviews and documents. Finally, frequency/percentage tables, central tendency measures, statistical analysis such as t-test and ANOVA/ANCOVA and content analysis were commonly used as data analysis.

This publication seeks to consider statistics education in Japan while referencing a student paper. The paper to be considered is as follows: "Gakusei no Koudou ga Gakusei Seikatsu Manzokudo ni Ataeru Eikyou" (The Influence of Student Behavior on the Degree of Satisfaction Perceived in Student Life).

LAK-18, the 8th International Conference on Learning Analytics and Knowledge, was hosted by the University of Sydney, Australia, from March 5 to 9, 2018. Traditionally, authors of papers that have received high scores through the review process of the conference are invited to extend their paper and submit it for a special issue of this journal. The chairs of LAK-18 have adopted a double-blind review process that has proven useful in maintaining the technical quality of the conference. Papers submitted to the special issue undergo a rigorous review process that is blind only, not double-blind, as a shorter version of the paper has been already published. The theme for LAK-18 was "Towards User-Centred Analytics". Though several of the papers receiving high scores in the review process explicitly dealt with this issue, it has been decided to devote a complete special issue of the journal to this theme and invite those papers in this special issue to come. Thus, the present special issue LAK-18 covers a broad range of themes, which reflect well the diversity and the vibrancy of the community, but excludes User-Centred Analytics. This editorial describes the story behind this special issue. This story begins with the contributions submitted to the conference and finishes with a short introduction to the papers in this issue. The two first episodes of the story, contribution type and country, include both tracks, research and practitioner. The reminder episodes refer only to the research track.

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games' content, leading them to develop a critical stance towards the game data model and representations.

Artificial intelligence (AI) is now affecting all aspects of our social lives. Without always knowing it, we interact daily with intelligent systems. They serve us invisibly. At least that is the goal we assign to them: to make our lives better, task by task. Artificial intelligence has the potential to make biology education more engaging, personalized, and effective by providing students with interactive simulations, personalized learning experiences, and other tools that help them understand complex biological concepts. In this paper, we discuss the integration of AI into the virtual classroom, which significantly enhances student learning experiences in various ways. The study shows that an effective integration of technology into the virtual classroom requires a thoughtful approach that aligns with educational goals and the specific needs of students. In fact, interactive simulations can help make biology more engaging and memorable for students. Besides, personalized learning AI algorithms can help biology students receive a more tailored and effective learning experience, helping them to better understand the course material and develop a deeper appreciation for the natural world. In this work, we will discuss the use of AI to enhance interactive simulation-based cellular processes, with additional application in anatomy, physiology, and ecology teaching. Moreover, this paper discusses how AI could be used to analyze student data and propose personalized learning using adaptive assessments, content recommendations, and data sciences. This paper illustrates examples of AI algorithms that could be useful for teaching biology.

This study explores how Deleuzian concepts of assemblage, affect, and rhizome can inform, and transform, our understanding of how technology shapes second language academic writing. More specifically, it focuses on the practice of L2 academic writing as novice writers compose a source-based research paper in an English for Academic Purposes bridging program. The study followed seven student participants over several weeks as they worked on this assignment. Data-collection instruments included ongoing interviews throughout the writing process, document analysis of written drafts, screencast recordings, and researcher diary. Rhizoanalysis was used to analyze the data and map connections between the elements operating in the student writers' assemblage. Vignettes are presented to problematize conventional reading of data and to turn attention to texts in L2 writing research as asignifying machines. Reading participant data differently allows for exploration of the interconnections between the material, affective, and expressive elements and forces in the digital mediated source-based writing process. For these novice L2 student writers, the elements and forces in their assemblage operate in unique and unpredictable ways, producing unforeseen outcomes.

This study examined ethnic differences in substance use and sexual behavior and whether drinking and drug use constitute risk factors for unsafe sexual practices among Native Hawaiian (NH), Caucasian, and Asian/Pacific Islander (API) high school students in Hawaii. A secondary data analysis of the Youth Risk Behavior Survey (1997 and 1999) using a representative sample of 2,657 students in 9-12 grades was performed. Chi-square tests for bivariate associations and multivariate regression analyses were conducted to identify predictors. NHs were more sexually active, initiated sex earlier, and tended to have multiple partners. Alcohol lifetime use was higher in NH, Caucasians, and males. NHs were more likely to initiate drinking by age 12 and engage more in episodic drinking. Non-episodic or episodic drinkers and students who use drugs were at risk to engage in sexual activity, have multiple partners, and use alcohol/drugs during sex. Risk was two times higher for substance use during sex if drinking was initiated at age 10 or younger compared to 15 years or older. Compared to abstainers, the risk doubled for lifetime multiple partners if drinking was initiated at ages 13-14. Overall, females were more sexually active. Because drinking was associated with sexual initiation and risky behavior, adverse effects of alcohol must be addressed in programs targeting underage drinking, thus helping youth delay of minimize sexual activity and prevent other associated problems. Intervention efforts should enhance life skills that endorse abstinence from alcohol and drug use.

Objective: To comparatively evaluate performances of tablet-based versus paper-based Venous Insufficiency Epidemiologic and Economic Study-Quality of Life/Symptom (VEINES-QOL/Sym) questionnaire. Methods: We prospectively evaluated 78 consecutive patients who completed tablet-based and paper-based VEINES-QOL/Sym questionnaires and compared their scores, completion time, data entry time, and ease of use. We used Student's t-test and Wilcoxon test for quantitative variables, Bland–Altman test and kappa coefficient for agreement between questionnaires and patients, respectively. Spearman's correlation coefficient was used to assess correlations. Results: Most participants (83.3%) found it easier to use the tablet device. Less time was needed to complete the tablet-based (median, 4.75; IQR, 3–7 min) than the paper-based (median, 8.3; IQR, 6.3–11.3 min) questionnaire (p <.001). Better educated patients took less time to complete paper-based (p =.003) and tablet-based (p =.001) questionnaires and considered the latter easier to use (p =.010). Conclusions: The tablet-based VEINES-QOL/Sym proved to be an easy-to-use and time-saving tool. [ABSTRACT FROM AUTHOR]

This study traces the evolution of computer-assisted language learning (CALL) by investigating published research articles (RAs) in four major CALL journals: ReCALL, Computer Assisted Language Learning, Language Learning & Technology, and CALICO Journal. All 2,397 RAs published over four decades (1983-2019) were included in the pool of data, and the Google Scholar citation metric was adopted to assess the impact of the papers. By selecting the top 15% of widely cited papers from each individual year, we minimized the time bias between years, enabling a balanced narration of the history of CALL through a representative dataset of 426 high-impact RAs. To identify the evolution of research trends, the contexts, methodologies, theoretical underpinnings and research foci of all 426 RAs were investigated using NVivo 12 and AntConc. The analysis of the data yielded encouraging results such as the upward trend in the number of publications and the international reach of CALL in the last two decades, the physical or virtual presence of language learners with diverse language profiles, and the growing tendency to triangulate methodology for increased complexity. However, long-standing issues such as the heavy reliance on traditional research contexts, poor reporting practices of basic demographic information, the large number of atheoretical papers and the concentration on a limited number of research foci continue to pose challenges in CALL research. Based on the findings, the paper suggests solutions for the controversies and addresses key issues for future research in CALL. [ABSTRACT FROM AUTHOR]

While computational biology and bioinformatics became an inherent part of most life science degrees, it remains challenging to encourage students to employ these diverse skills in a practical research context. In this education paper, we present a method to enable students to acquire hands-on skills by the data-driven study of complex biomolecular datasets. The course starts with a series of introductory lectures, including a practical session on Cytoscape and associated computational network biology techniques. As we believe that enabling students to work on their own research questions will increase their motivation and interest, we introduced a recent hallmark virus-host protein-protein interaction article from which the database served as a starting point for the students to develop their research goal, which was presented and discussed in a first informal meeting. Over the next two months, about four interactive sessions were held, where students presented their problems, progress and challenges and exchanged ideas to help each other reach their goals. The role of the lecturers was to assist the students with independently collecting data and exploring new software tools for their research question. We present this concept and methods, and we share our experiences from an evaluation of the student learning outcomes.

Egocentric networks represent a popular research design for network research. However, to what extent and under what conditions egocentric network centrality can serve as reasonable substitutes for their sociocentric counterparts are important questions to study. The answers to these questions are uncertain simply because of the large variety of networks. Hence, this paper aims to provide exploratory answers to these questions by analyzing both empirical and simulated data. Through analyses of various empirical networks (including some classic albeit small ones), this paper shows that egocentric betweenness approximates sociocentric betweenness quite well (the correlation is high across almost all the networks being examined) while egocentric closeness approximates sociocentric closeness only reasonably well (the correlation is a bit lower on average with a larger variance across networks). Simulations also confirm this finding. Analyses further show that egocentric approximations of betweenness and closeness seem to work well in different types of networks (as featured by network size, density, centralization, reciprocity, transitivity, and geodistance). Lastly, the paper briefly presents three ideas to help improve egocentric approximations of centrality measures.

As middle school mathematics becomes more abstract, it is imperative for teachers to introduce concepts in ways that are interesting and meaningful to students. Since her students struggled at times to stay engaged in mathematics and seemed to have difficulty developing conceptual understanding, the author looked for ways to create learning contexts that would be noteworthy and relevant. Working with data seemed to lend itself readily to contexts that were appealing to students. To give her students additional experience with measures of central tendency and data collection, she developed an activity that involved making and flying paper airplanes. This lesson, which ultimately focused on measures of central tendency, also addressed measurement, scatter plots, and the four main forces of flight. (Contains 5 figures.)

This paper discusses about the scatter of the intensity of vibration signals of paper prints and analyses their mechanical parameters applying the theory of covariance functions. It is an important practical problem, before starting printing process of colour prints, expecting the correct position of fixed raster points, to adjust the paper sheet tension between printing machine sections. The results of measuring the intensity of vibration signals at the fixed points were presented on a time scale in the form of arrays (matrices). The estimates of cross-covariance functions between digital arrays result in measuring the intensity of vibrations, and the estimates of auto-covariance functions of single arrays were calculated upon changing the quantization interval on the time scale. Application of normed auto-covariance and crosscovariance functions enables reduction of preprinting experimental measurements, which saves time (what is actual for industry). Tension force depends on the mechanical properties of the paper sheet and print. These characteristics depend on paper type, layers of printing colors and positioning of the coverage. In the calculation, the software Matlab 7 in batch statement environment was applied.

Science instructors are increasingly incorporating teaching techniques that help students develop core competencies such as critical-thinking and communication skills. These core competencies are pillars of career readiness that prepare undergraduate students to successfully transition to continuing education or the workplace, whatever the field. Course-based undergraduate research experiences that culminate in written research papers can be effective at developing critical-thinking and communication skills but are challenging to implement as class size (and student-to-instructor ratio) grows. We developed a hierarchical mentoring program in which graduate student mentors guided groups of four to five undergraduate students through the scientific process in an upper-level ecology course. Program effectiveness was evaluated by grading final research papers (including previous year papers, before the program was implemented) and surveys (comparing to a course that did not implement the program). Results indicated that primary benefits of hierarchical mentoring were improvements in perceived and demonstrated ability in data analysis and interpretation, leading to a median increase in paper score of ~10% on a 100-point scale. Future directions indicated by our study were a need to incorporate more approaches (e.g., low-stakes writing exercises) and resources into a revised program to improve outcomes for students whose primary language is not English.

Although various academic disciplines use data papers to support effective research practices, data papers are still uncommon in the educational sciences. Main goals of data papers are enhancing transparency regarding research processes and supporting data sharing among researchers and thus, open science. As many educational research projects include personal data often of minors, publishing raw data can be challenging due to privacy regulations and laws (GDPR). The present article aims at exploring how a data paper can contribute to open science and enhancing transparency regarding research and publication processes for educational research projects, often containing personal data that cannot be made openly available in its raw form. To this end, we describe a dataset of a research project on teachers' noticing as basis for their classroom management. As this project includes rich process-tracing recording methods, the dataset is diverse and serves as basis for multiple analyses and publications while containing personal data of teachers and minors. By elaborating on the characteristics of the dataset, its gathering, analysis approaches and sharing preprocessed and anonymized data files, this data paper explores how to contribute to transparency and open science in educational sciences while acting within the boundaries set by privacy regulations and laws. [ABSTRACT FROM AUTHOR]

The main aim of this work is to provide insight into a bibliometric analysis of Data Journals and Data Papers in terms of research areas, disciplines, publication year and country. In particular, we calculated many bibliometric indicators, especially: the number of publications and citations. Furthermore, this work also investigated the top 20 journals in which scientists published the largest number of Data Papers. It was found that the first Data Paper in Medical and Health Science was published in 2006. Our results indicate that about 64% of Data Papers are cited and about 30% of Data Papers are articles that have international co-authors. We also found that a significant number of Data Papers were published in top quartile (Q1) Open Access journals. Furthermore, most Data Papers were published in Multidisciplinary Sciences, followed by Biological Sciences, Earth and related Environmental Sciences as well as Computer and Information Sciences. Our analyses show that the largest number of Data Papers were published in journals with an Impact Factor ranging from 5 to 6. Additionally, the largest number of Data Papers were published in the USA, China, the United Kingdom, Italy and France. From the point of view of scientists, this work is needed because scientists can check information about journals in which they may publish research data. Moreover, they can find Data Journals with a high Impact Factor and MEiN points. This analysis is also interesting because scientists/publishers can compare the disciplines and countries in which researchers have published the greatest number of Data Papers. Finally, it is worth noting that this work gives insights into the various bibliometric indicators related to citations and the number of publications. This information can be useful for people associated with bibliometric analyses. [ABSTRACT FROM AUTHOR]

How do we see young children's thinking in science? Is it, as much previous research has led us to believe, that their ideas can be neatly boxed like "brown paper packages tied up with strings"--as the song from "The Sound of Music" goes? Or are their ideas like "wild geese that fly with the moon on their wings" ("Sound of Music"): fluid, complex, rich...? Drawing on the author's research into young children's ideas about natural phenomena such as the rain and clouds, and on Rogoff's three foci of analysis (personal, interpersonal and contextual), this paper illustrates how a consideration of sociocultural theory can be useful in framing research with young children, and allow us to see beyond the boxes. Emphasis is placed on recognising that children's thinking in science is embedded within particular sociocultural contexts, is guided by others and integrated with their use of certain mental and physical cultural tools. Thus, the article aims to present an alternative method for the generation of data on young children's thinking. Specific analysis of this data will, it is intended, be presented in a subsequent article.

Scientific literacy is an important fundamental skill for scientists. In order for students to improve their writing they require constant feedback throughout the process. They also need to learn the value of the peer review process by experience. Although they may require submission of a rough draft, typical upper-division writing assignments such as term papers and formal lab reports are usually due at the end of the semester and are not returned to the student until after the semester is finished; this precludes the establishment of a dialogue between the instructor and the student that can lead to improvement of the work. In addition, achieving scientific literacy through the critical analysis of journal articles is also a learned skill that requires constant feedback and guidance. This article describes a series of mini-assignments that culminate in a formal article and informal presentation. These assignments are designed to proceed through one semester of an upper-division chemistry course and to provide multiple opportunities for instructor feedback to the student both on written work and in discussion of the literature. (Contains 2 tables.)

Western Kentucky University Libraries developed a survey similar to its Web-based library satisfaction survey, with identical content for library Web and exit patrons to compare these groups' responses. Focuses on the collection of Web and exit survey responses in a two-week period, transformation of response data for analysis, comparison of the two samples, and discussion of potential use of results. (AEF)

Examined whether computer assisted self-interview (CASI) alcohol, tobacco, and drug use surveys are feasible with 2,296 7th, 9th, and 11th graders in 2 communities. CASI surveys did not increase reported rates of substance abuse, but did improve the speed of data processing and decrease missing data. (SLD)

Analyzing a conference, especially one as young and focused as LAK, provides the opportunity to observe the structure and contributions of the scientific community around it. This work will perform a Scientometric analysis, coupled with a more in-depth manual content analysis, to extract this insight from the proceedings and program of LAK 2013. Authorship analysis reveals an open and international community, while internal citation analysis provides evidence of the construction of a body of knowledge central to Learning Analytics. The analysis of the content of the papers found five main topics: visualization, behaviour analysis, social learning analytics, learning analytics for MOOCs, and learning analytics issues (ethical, scalability, etc.), as well as papers reflecting on the field itself. We discuss representative papers presented at the conference, highlighting trends and new developments. Learning analytics is a diverse multidisciplinary field with an emerging interdisciplinary core, well situated to benefit from productive dialogue concerning its scope and purpose and to reflect on the pedagogies and epistemologies implied by its methods.

The proliferation of information technology tools in higher education has resulted in an explosion of data about students and their contexts. Yet, current policies governing these data are limited in their usefulness for informing students, instructors, and administrators of their rights and responsibilities related to data use because they are based on antiquated conceptions of data and data systems. To understand how data privacy policies conceptualize and represent data, privacy, student agency, and institutional power, we conducted a policy discourse analysis of 151 university policy statements related to student information privacy and the responsible use of student data from 78 public and private post-secondary institutions in the U.S. Three common discourses emerged: educational records are static artifacts, privacy solutions are predicated upon institutional responsibility and student agency, and legitimate educational interest in data are institutionally defined and broadly applied. We explore the assumptions, biases, silences, and consequences of these discourses and offer counter- discourses to begin a foundation for the development of privacy policies in a new data age.

Investigates the structure of information in all sections of the medical research paper. Findings identify an eleven-move schema, out of which nine were found to be "normally required" and two "optional." Each schema was found to embody "constituent elements" and to be characterized by distinct linguistic features. (36 references) (Author/CK)

Near Infrared Spectroscopy (NIRS), as one of analysis technologies, has shown promisingly industrial applications for significant properties for recent decades such as fast response, preciseness, non-intrusion etc. Here, the authors employed NIRS coupled with a series of physical and chemical tests to assess the ageing condition of oil-paper insulation, which is responsible for the main insulation type of high-voltage power transformer. Among these procedures, the data analytic algorithms are of utmost importance to determine the evaluation precision. After plenty of trial-and-error, Savitzky-Golay (S-G) convolution was finally utilised to de-noise samples and improve the spectral data quality. The competitive adaptive reweighted sampling (CARS) was used to select the optimal wavelength combination of NIRS, which is found able to fully extract the effectively spectral information and reduce dimensions of spectral data. Based on the above-mentioned techniques, the quantitative analysis model of NIRS was established by partial least squares (PLS), which could synthetically process the spectral data and the degree of polymerisation (DP) of paper samples. The results indicated that compared with the traditional detection methods, the NIRS analysis is a powerful and informative tool to characterise the condition of oil-paper insulation without intrusion or damage to transformers.

This paper presents a unique advanced statistical approach based on Artificial Intelligence (AI) to examine factors affective on phonological awareness and print awareness of preschool children. Artificial Neural Network (ANN) models were created and correlations between the independent and dependent (outcome) variables were analyzed. The ANN models were trained using the data for phonological awareness and print awareness of children. According to the findings, the created ANN model had an excellent fit to the actual data (R[superscript 2] = 0.934 and 0.940). Furthermore, the ANN model results were tested with a traditional analysis technique, Pearson correlation analysis. The ANN models yielded similar results to the Pearson correlation analysis but with more detail as expected. The ANN models were run for user-generated synthetic datasets and the relationships between the dependent and independent variables were discussed using model results. Demographic variables, namely, children's age, mother's age, mother's education, and family income were found to be not effective on children's print and phonological awareness skills. On the other hand, home literacy environment-related variables were found to be very effective. In conclusion, this paper introduces a methodology for implementing ANN modeling in educational data. A novel and powerful approach is provided to assess and estimate essential components of early literacy skills. The study has important implications for advancing our understanding of potential benefits of employing an AI-based modeling techniques in the field of education. The utilization of machine learning methods in educational research, as presented in this paper, has the potential to fundamentally reshape our approaches in categorizing and analyzing educational data.

Purpose: The purpose of this study was to investigate associations between digital urine color and paper urine color with other urine indices to assess hydration status. Methods: Twelve male subjects (mean ± standard deviation; age, 26 ± 8 years; body mass, 57.8 ± 5.3 kg; height, 177.5 ± 8.9 cm; VO2max, 57.8 ± 5.8 ml·kg−1·min−1) performed four exercise trials in the heat. Before and following exercise trials, subjects provide urine samples. Urine samples were measured using a digital urine color chart on a portable device screen. Urine samples were also assessed with urine specific gravity (USG), urine osmolality (UOsmo), and a validated paper urine color chart. Results: There were extremely large associations found between digital urine color and paper urine color (r = 0.926, p < 0.001). Correlation coefficients showing associations with USG and UOsmo were similar between digital urine color (USG, r = 0.695, p < 0.001; UOsmo, r = 0.555, p < 0.001) and paper urine color (USG, r = 0.713, p < 0.001; UOsmo, r = 0.570, p < 0.001). Bland–Altman analysis indicated that no proportional bias was observed between digital and paper urine colors (bias, − 0.148; SD of bias, 0.492; 95% LOA, − 1.11, 0.817; p = 0.094). Conclusions: Strong associations were found between digital and paper urine colors with no proportional bias. Furthermore, the degree of associations with USG and UOsmo was similar between digital and paper urine color. These results indicate that digital urine color is a useful tool to assess hydration status and this method could be used as an alternative method to using paper urine color. [ABSTRACT FROM AUTHOR]

Scientometrics indicators are used to assess scientists, universities and research institutes for scientific policy-making. The aim of this research was to assess the status of top Iranian medical researchers using scientometric indicators. The study was carried out using scientometric methods. The statistical population included the top 500 Iranian researchers in the field of medicine who were ranked in the Iranian Scientometric Information Database (ISID) based on some scientometric indicators. The data were analyzed using SPSS 22 software and Pearson's correlation coefficient, stepwise regression analysis and Chi-square tests were applied for data analysis. Findings revealed that each researcher had an average h index of 24.04, g index of 40.15, and i10 index of 90.79. There was a positive significant relationship between the number of internationally-collaborated papers and the number of citations received (r = .606, p < .01). The stepwise multiple regression analysis showed that the three variables of paper number, citation counts and mean citation rate determined 72 % of the changes in the h-index. It can be concluded that researchers publishing more papers are more likely to be cited. However, their scientometric indexes are not always better than those of other scientists. [ABSTRACT FROM AUTHOR]

California legislature made a policy change with Senate Bill (SB) 1644 (2000), shifting Cal Grant Programs to focus on entitlement; counter to the national trend of merit based grant programs. This article describes a study examining effectiveness and extent to which SB 1644 is meeting its legislative objectives: increase in higher education opportunities and lower student loan debt. Additionally, demographic characteristic differences of student populations seeking higher education opportunities (20-year period) and factors influencing California policy to embrace entitlement grants are presented. The national implication and political (value) question derived from this study was: Is higher education a right or a privilege?

Taking as an example the very interesting and motivating paper by Sousa Ribeiro et al. (2021) an attempt is made of providing a couple of insights into the decision making process from the point of view of the potentially helpful aspects of data analysis and OR-related modelling. These are just hints and suggestions, meant primarily to emphasise the multifaceted character of the decision making situations and processes, especially when concerning more complex issues. While the course of the procedure proposed and exemplified in Sousa Ribeiro et al. (2021) is treated as fully correctly and successfully carried out to the end, we wish to show the potential use of information, constituting in a sense a "by-product" of such a procedure, or, actually, of any similar procedure, aimed at supporting decision making. [ABSTRACT FROM AUTHOR]

This volume includes 3 papers based on presentations at a workshop on communicating assessment information to particular audiences, held at Educational Testing Service (ETS) on November 4th, 2010, to explore some issues that influence score reports and new advances that contribute to the effectiveness of these reports. Jessica Hullman, Rebecca Rhodes, Fernando Rodriguez, and Priti Shah present the results of recent research on graph comprehension and data interpretation, especially the role of presentation format, the impact of prior quantitative literacy and domain knowledge, the trade-off between reducing cognitive load and increasing active processing of data, and the affective influence of graphical displays. Rebecca Zwick and Jeffrey Sklar present the results of the Instructional Tools in Educational Measurement and Statistics for School Personnel (ITEMS) project, funded by the National Science Foundation and conducted at the University of California, Santa Barbara to develop and evaluate 3 web-based instructional modules intended to help educators interpret test scores. Zwick and Sklar discuss the modules and the procedures used to evaluate their effectiveness. Diego Zapata-Rivera presents a new framework for designing and evaluating score reports, based on work on designing and evaluating score reports for particular audiences in the context of the CBAL (Cognitively Based Assessment of, for, and as Learning) project (Bennett & Gitomer, 2009), which has been applied in the development and evaluation of reports for various audiences including teachers, administrators and students.

Businesses and other organizations across the globe are becoming more and more data-driven, using a combination of descriptive, diagnostic, predictive and prescriptive analytics to gain a strategic advantage through understanding the past, what we hope to happen in the future, and the ability to accurately predict future outcomes. These forms of analytics span from basic statistical summaries and data visualization to artificial intelligence models. Many organizations are now requiring new job applicants, new hires, and existing employees to be data literate. As such, it is becoming incumbent on teachers, students, and practitioners to possess some basic knowledge or experience in business analytics, at least within their educational and functional domains. Current best-practice in business school curriculum embeds some form of analytics across the curriculum. Unfortunately, many business colleges do not have the experience or resources to do so, hence teachers are unprepared to teach, and students are not prepared to enter the business world being data literate. While higher levels of analytics can be statistically intimidating, there are numerous applications of analytics that do not require statistics or higher-level models. This paper introduces one such technique practiced within marketing education and industry since 1995 and is called RFM. RFM has long been known in marketing curriculum and practice but has seen virtually no exposure in business schools outside of marketing major courses. This reflects an unintended consequence of teaching and learning within "functional" silos. It is hoped that teachers and students across the business curriculum, as well as workforce participants, can use this case to gain an appreciation of data literacy and analytics toward application within any functional area of business. The purpose of this paper is to avail those outside of marketing education and practice with an effective, easy to understand, easy to apply model, with no statistics involved. The goal is to facilitate increased data literacy and interest in understanding and/or applying analytics to other functional arear of business. RFM is not unique to this paper but is aimed at broadening teacher, student and workforce participant experience and knowledge of business analytics.

Collaboration is a complex, multidimensional process; however, details of how multimodal features intersect and mediate group interactions have not been fully unpacked. Characterizing and analyzing the temporal patterns based on multimodal features is a challenging yet important work to advance our understanding of computer-supported collaborative learning (CSCL). This paper highlights the affordances, as well as the limitations, of different temporal approaches in terms of analyzing multimodal data. To tackle the remaining challenges, we present an empirical example of multimodal temporal analysis that leverages multi-level vector autoregression (mlVAR) to identify temporal patterns of the collaborative problem-solving (CPS) process in an immersive astronomy simulation. We extend previous research on joint attention with a particular focus on the added value from a multimodal, temporal account of the CPS process. We incorporate verbal discussion to contextualize joint attention, examine the sequential and contemporaneous associations between them, and identify significant differences in temporal patterns between low- and high-achieving groups. Our paper does the following: 1) creates interpretable multimodal group interaction patterns, 2) advances understanding of CPS through examination of verbal and non-verbal interactions, and 3) demonstrates the added value of a complete account of temporality including both duration and sequential order.

Summarizes 19 papers presented at the Fourth International Conference on Teaching Statistics held in Morocco, July 1994. Papers presented were in five categories: (1) empirical studies on students' conceptions; (2) theoretical papers on teaching and learning; (3) assessment; (4) using computers in teaching probability and statistics; and (5) data analysis. (MKR)

Purpose: The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens. Design/methodology/approach: This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data. Findings: Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness. Originality/value: Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.

Underlying thematic analysis are a few fundamental human cognitive processes, such as categorizing, prototyping and metaphorical mapping. By unpacking these basic processes of human cognition, this paper hopes to provide a cognitive basis for thematic analysis as a foundational method in data analysis for qualitative research. In particular, it hopes to address the gap between qualitative methodologists' assumption of thematic analysis as a subjective, creative and flexible process and editors/reviewers' expectation that thematic analysis shall be objective, reliable and rigorous. By consciously and purposefully applying these cognitive processes, thematic analysis can be subjective and yet disciplined, creative and yet rigorous, flexible and yet reliable. The ultimate goal of this paper is to demystify, delineate and further demarcate the thematic analysis process for young and novice qualitative researchers.

Managing and analysing large qualitative datasets pose a particular challenge for researchers seeking a consistent and rigorous approach to qualitative data analysis. This paper describes and demonstrates the development and adoption of a matrix tool to guide the qualitative data analysis of a large sample (N = 122) of interview data. The paper articulates the theoretical and conceptual underpinnings of the matrix analysis tool and how it was developed and applied in a longitudinal mixed methods out-of-home-care research study. Specific illustrations and examples of data integration and data analysis are provided to demonstrate the benefits and potentials of constructing matrix tools to guide research teams when working with large qualitative data sets alone or in combination with quantitative data sets.

The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and encompassing 75 papers, analysed the applications of Big Data and analytics in higher education. The focus was on their usage in supporting learning, teaching and administration as reported in papers indexed in SCOPUS, Web of Science and IEEE Xplore. The key findings from the review revealed that Big Data and analytics are predominantly used to support learning and, to a lesser extent, guide teaching and informing administrative decision-making processes. The review also identified a set of studies focused on supporting student well-being. Further, we extend the use of Big Data in higher education to include the well-being of students and staff. This paper contributes to the growing debate on the practical use of Big Data and analytics to provide valuable insights for solving systemic challenges facing high education in the twenty-first century.

Purpose: This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India. Design/methodology/approach: Study employs an in situ experiment using a conceptual model based on learning theory. The participant's end-of-semester GPA is Performance Indicator. Integrating PBL with classroom teaching is unique instructional approach to this study. An unsupervised and supervised data mining approach to analyse PBL impact establishes research conclusions. Findings: The administration of PBL results in improved learning patterns (above-average) for students with medium attendance. PBL, Gender, Math background, Board and discipline are contributing factors to students' performance in the decision tree. PBL benefits a student of any gender with lower attendance. Research limitations/implications: This study is limited to course students from one institute and does not consider external factors. Practical implications: Researchers can apply learning patterns obtained in this paper highlighting PBL impact to study effect of every innovative pedagogical study. Classification of students based on learning behaviours can help facilitators plan remedial actions. Originality/value: 1. Clustering is used to extract student learning patterns considering dynamics of student performances over time. Then decision tree is utilized to elicit a simple process of classifying students. 2. Data mining approach overcomes limitations of statistical techniques to provide knowledge impact in presence of demographic characteristics and student attendance.

In today's university era, reforming the English teaching model has become a major research topic for researchers. Based on this, this paper adopts a hierarchical and progressive model construction method to further explore the reform model of university English teaching in the context of educational ecology. First, this paper discusses the development of the layered progressive approach in various countries and the current status of the layered progressive approach. By allowing the use of the model of the layered progressive approach, the data related to the reform of university English teaching were analyzed and organized. The combination of internal optimization, data-theoretic learning algorithms, and data federation algorithms for the model of the hierarchical incremental approach are also investigated. The results of the study show that the layered progressive model of college English teaching reform has good results through specific practical applications in a strengthened educational ecosystem.