This dataset was collected through a survey conducted in 2021 and includes responses from 349 students (boys and girls) attending lower secondary schools in Norway. The primary objective of the data collection was to investigate how task difficulty labels influence students’ self-efficacy and performance in mathematics, with particular attention to gender differences. Variables included in the dataset: (1) Gender, (2) Self-efficacy related to three mathematics tasks, measured both before and after the tasks were presented with difficulty labels, (3) Performance on the same three mathematics tasks, (4) Task difficulty labels assigned to each task (experimental variable: easy, medium, or difficult). This dataset enables analysis of how labelling mathematics tasks as “easy”, “medium”, or “difficult” affects students’ self-efficacy and performance, and how these effects may differ across genders.

Dataset used in a meta-analysis examining the effects of educational technology on mathematics outcomes. Includes effects from 40 studies with codes for study and methodological features.

Math anxiety involves moderate to extreme fear, anxiety, and occasionally physical pain associated with anticipating or performing mathematical tasks. High levels of math anxiety have been tied to students taking lower levels of math and choosing less quantitatively challenging courses and careers. In a small geoscience program in a primarily undergraduate university, math anxiety has been assessed using a standardized math anxiety rating survey embedded into a more general anxiety survey. An intervention that involves re-phrasing geoscience-focused quantitative word problems was used on both low- and high-stakes assessments. In courses with no intervention at both the major and general education levels, students were found to have similar math anxiety ratings and no significant change over the semester. In contrast, students in the intervention major courses were statistically more likely to have a drop in their math anxiety when compared to the large control and also when compared to a smaller control of similar-level courses. In a geoscience classroom, rephrasing quantitative questions to focus more on geoscience knowledge versus the quantitative task appears to be a viable way to lower math anxiety while giving students’ experience to build their quantitative skills.

The understanding of fractions can be challenging for primary school students. Music, like mathematics requires an understanding of subdivisions of numbers and proportional reasoning. Musical experience has been shown to enhance cognitive skills and most children like music (in contrast to maths). Yet, experimental work that has integrated the teaching of mathematical and musical concepts is scarce. In this study, we tested the feasibility of PlayMyMath in UK schools, an innovative EdTech approach that combines the teaching of fractions with the building of musical rhythms. We employed a mixed-methods approach: a qualitative strand explored implementation of PlayMyMath with teaching staff and a quasi-experimental pretest/post-test design study examined whether PlayMyMath can improve fraction skills in Year 4 students in the UK. We envisage this study as a precursor to further pilot and efficacy studies that will allow us to test the intended outcomes in a larger population.Uploaded here is the quantitative data derived from this project in an excel format.

A unified introductory science curriculum that fully incorporates mathematics and quantitative thinking aimed to incorporate biology into the traditional quantitative cultures that have come to define the physical sciences and engineering.

This dataset contains the deidentified data used in the validation process for the Math-Biology Values Instrument (MBVI). MBVI_spring2016 contains data collected from undergraduate life science majors (via electronic survey) to develop the MBVI and was used for exploratory factor analyses and establishing convergent and divergent validity. MBVI_fall2016 contains data collected from a second independent sample of undergraduate life science majors (also via electronic survey) that was used for confirmatory factor analyses. The two "key" files contain survey item text, response options, and notes for all column headings in the data files. A full description of the data collection process and analyses can be found in the related publication cited below.

Code and associated data for the following preprint:

AP Browning, JA Sharp, RJ Murphy, G Gunasingh, B Lawson, K Burrage, NK Haass, MJ Simpson. 2021 Quantitative analysis of tumour spheroid structure. eLife http://dx.doi.org/https://doi.org/10.7554/eLife.73020

Data comprises measurements relating to the size and inner structure of spheroids grown from WM793b and WM983b melanoma cells over up to 24 days.

Code, data, and interactive figures are available as a Julia module on GitHub:

Browning AP (2021) Github ID v.0.6.2. Quantitative analysis of tumour spheroid structure. https://github.com/ap-browning/Spheroids

(copy archived here)

Code used to process the experimental images is available on Zenodo:

Browning AP, Murphy RJ (2021) Zenodo Image processing algorithm to identify structure of tumour spheroids with cell cycle labelling. https://doi.org/10.5281/zenodo.5121093

We report on three approaches taken to incorporate collaborative activities into undergraduate mathematics classes. There is strong evidence from research in K-12 classrooms that these, and similar, approaches support a range of positive learning outcomes for students. Despite the potential benefits the cited studies have shown, research into the use of such methods at the tertiary level is limited. We describe the ways in which we have implemented research projects, collaborative tutorials, and team-based learning in a range of undergraduate mathematics classes in two countries. We present quantitative and qualitative evidence from these teaching experiences to support our claim that there is a definite mandate for significant opportunities within our courses for students to work cooperatively, talk together, and argue about mathematics.

The repository contains the following files:

• "Research_Data" -> Data of our research trial
• "RESULTS_testequating" -> output of the procedure of test equating implemented using JMetrik
• "results41_CTonCN" -> Results of the Rasch Model implemented using ConQuest considering the 41 items of the CoreTest on the National Population Survey
• "results41_CTonER" -> Results of the Rasch Model implemented using ConQuest considering the 41 items of the CoreTest on the Emilia Romagna regional Population
• "results41_CTonP" -> Results of the Rasch Model implemented using ConQuest considering the 41 items of the CoreTest on our entire reserch population
• "results41_CTonPV" -> Results of the Rasch Model implemented using ConQuest considering the 41 items of the CoreTest on our reserch population who answered to the varied form of the test
• "results41_CTonPO" -> Results of the Rasch Model implemented using ConQuest considering the 41 items of the CoreTest on our reserch population who answered to the original form of the test
• "resultsINVALSI_OriginalTest" -> Results of the Rasch Model implemented using ConQuest considering the INVALSI test on on the National Population Survey
• "resultsO_TOonPo" -> Results of the Rasch Model implemented using ConQuest considering the whole original test on our reserch population who answered to the original form of the test
• "resultsV_TVonPV" -> Results of the Rasch Model implemented using ConQuest considering the whole varied test on our reserch population who answered to the original form of the test

For all the results files we can also make available the related "summary" file, containing the summary of the estimation parameters (conquest output).

This dataset was collected as part of a study exploring high school mathematics teachers’ perceptions and use of artificial intelligence, with a particular focus on the perceived usefulness and perceived ease of use of artificial intelligence (AI) in teaching. ChatGPT was used as the artificial intelligence technology used in this study. The study employed a sequential explanatory mixed-methods design, guided by the Technology Acceptance Model 3 as a theoretical framework. Quantitative data were gathered through an online survey, in which structured Technology Acceptance Model 3 questionnaires were adapted and administered to examine participants' perceived usefulness and perceived ease of use of artificial intelligence, as well as the determinants. The quantitative data were analysed using the Statistical Package for the Social Sciences version 26. Descriptive statistics was used to interpret the data.Qualitative data were obtained through classroom observations and semi-structured interviews. Observations focused on how participants use artificial intelligence in their teaching, while the interviews provided deeper insights into their experiences and perspectives. All observations and interviews were recorded and subsequently transcribed for the dissertation. In order to open this data, Microsoft Excel, an MP4 video player, an audio player, and a portable document format reader will be needed.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for spanish with minor in mathematics a related quantitative field in the U.S.

Gateway courses are foundational prerequisite courses that undergraduate students must complete prior to enrolling in major courses (e.g., first-year mathematics, chemistry, psychology, statistics). Gateway courses often have high enrolment, and provide less support, structure, and feedback compared to previous experiences (e.g., secondary school). Declines in students' motivation and performance are common. This PhD project investigated two sources of engagement and motivation: self-efficacy and interest across two mathematics gateway courses. In particular, factors related to how self-efficacy and interest changed during the courses were examined across the studies. Four studies were conducted across five offerings of these two courses from 2020-2022. Participants were students enrolled in these courses. Study 1 (n=175; Sept-Dec 2020; Course 1) was conducted in an online (pandemic) setting. The interplay between students' (amounts of) self-efficacy, interest, and performances (i.e., quizzes) across the course was investigated. Study 2 (n=349; Sept-Dec 2021; Course 1) was conducted the next year, and examined how overall self-efficacy changes, and how those changes were associated with performances across a course, and interest at the end of the course. Study 3 (n=313; Sept-Dec 2021; Course 2) investigated short-term changes in interest, and how they were related to performance, and self-efficacy. Lastly, Study 4 contained two studies (n=299; n=407; Studies 4a, 4b; Courses 1 & 2) that investigated the interplay between perceived difficulty on performance tasks (i.e., quizzes), short-term changes in self-efficacy, performances, and interest (in the second study).The data files are the datasets used to conduct the analyses across the four studies. These included students' responses on formative quizzes, and self-reported data on self-efficacy, interest, perceived difficulty, and gender. These data were used for quantitative analysis using MPlus and other software. Each folder contains the relevant files each study (presented in the respective chapter of the thesis).1) Chapter 3 - Study 1 contains the dataset used for the first study. This study is already published.2) Chapter 4 - Study 2 contains the datasets used for the second study, including for the full model, invariance and reliability testing, and dataset for IRT.3) Chapter 5 - Study 3 contains the datasets used for the third study, including for the full model and dataset for IRT.4) Chapter 6 - Study 4 (Studies 4a and 4b) contains the datasets used for the last study, including those used for the full model, dataset for IRT, and perceived difficulty.

headinclination_allmodels.xlsx:Excel file containing the raw data and calculated angles for sunflower head inclination measurements. This dataset includes:Plot identifiers and location informationPlant measurements (flower height, maturity height, stem length, head height)Calculated values (height difference, incline length)Angle measurements using different methods (trigonometry-based, linear model)supplementarymaterial_angle 20240619.stl:STL file containing a 3D model related to the angle measurement apparatus used in the study.supplementarymaterial_mount 20240619.stl:STL file containing a 3D model of the mounting mechanism used for the measurement apparatus.

Abstract

High quality postgraduate training in science, technology, engineering and mathematics (STEM) related disciplines in sub-Saharan Africa (SSA) is important to strengthen research evidence to advance development and ensure countries achieve the Sustainable Development Goals (SDGs). Equally, participation of women in STEM careers is vital, to ensure that countries develop economies that work for all their citizens. However, women and girls remain underrepresented in STEM due to gender stereotyping, lack of visible role models, and unsupportive policies and work environments. Therefore, there is a need to consolidate information on participation and experiences of women in STEM related postgraduate training and careers in SSA to enhance their contribution to realizing the SDGs. The primary objective of this study is to examine the participation and experiences of women in postgraduate training, and their subsequent recruitment, retention and progression in STEM careers in East Africa. A secondary objective is to establish the gender gaps in training and career engagement in selected STEM related academic disciplines in East Africa. The descriptive study will employ a mixed methods approach, including a scoping review, qualitative interviews, and quantitative analysis of secondary data. We will synthesize results to inform the development of an effective gendered approach and framework to improve participation and experiences of women in STEM training and career engagements in SSA. We will conduct the study over a period of five years.

Geographic coverage

Regional coverage (East Africa Region)

Analysis unit

Individual Women in STEM

Universe

Qualitative data: Women in Science Technology Engineering and Mathematics (STEM) in postgraduate training and career Quantitative data: Postgraduate students, faculty, reseachers and supervisors (both men and women) in STEM in Inter-University Council for East Africa (IUCEA) member Universitiies

Sampling procedure

The study utilized a purposive sampling technique and targeted all universities that offered doctoral programs in applied sciences, technology, engineering, and mathematics. At the time, only 23 of the 74 universities in Kenya—equivalent to 30%—offered doctoral degrees in STEM. It was assumed that a similar or lower percentage would be found in the other five countries, namely Uganda, Tanzania, Rwanda, Burundi, and South Sudan.

Purposive sampling was used to recruit participants from purposively selected universities and national higher education commissions and agencies for the study. In universities, all students enrolled in doctoral programs in STEM were considered. Additionally, female and male students' lecturers, supervisors, mentors, and other faculty members and researchers in the identified institutions were also considered for participation in the study.

Purposive sampling of doctoral students, faculty, and early career researchers (post-doctoral fellows within the first six years since receiving their PhD) was conducted using the following inclusion criteria:

Inclusion criteria i. Worked in a STEM field/discipline ii. Enrolled in a doctoral program within a STEM field iii. Early career researchers in a STEM field in research organizations iv. Faculty in a STEM field at a university

Additionally, registrars, postgraduate training coordinators, heads of departments, and officials from national agencies and ministries related to postgraduate training and research were purposively selected from all the identified universities to provide input on existing policies, guidelines, and enrollment data. For each of the mentioned groups, 7-12 interviews were conducted, totaling 60 interviews.

Sampling deviation

Qualitative For the Key informant interviews one participant was interviewed from the engineers board despite the scope being Inter-University Council for East Africa (IUCEA) member Universities.

Quantitative The online survey was completed by some researchers not working/teaching in IUCEA member universities

Mode of data collection

Other [oth]

Research instrument

Quantitative data collection A. Online Survey This was carried out through an online survey questionnaire that was circulated via email and other digital platforms such as WhatsApp. The questionnaire had various parts: Part A - Participants characteristics This section mainly collected demographic details such as age, gender, nationality, residence, marital status, income, highest level of education completed, year of study, supervision and mentoship relationship, field of study in STEM (Science, Technology, Enginnering and Mathematics), mode of funding of postgraduate degree,

Part B - Status of Gender equality This section collected information on students enrollment and graduation in masters and PhD in STEM looking at gender distribution,

Part C - Factors that contribute to participation of women in STEM This section collected information on the factors or situations encountered while pursuing career in STEM in your specific discipline

Part D - Strategies for Optimizing Women's Engagement in STEM This section collected information on the strategies can maximize engagement of women in STEM training PhD level and subsequent careers

Part E - Effect of the COVID-19 pandemic on women's progression In this section collected information on COVID-19 pandemic affect on research progress or deadline for submission of thesis, COVID-19 pandemic affect on current research funding, COVID-19 pandemic caused researchers to work from home, working from affected progress in studies, any direct responsibilities caring for children, number of children being taken care of, change of domestic work responsibilities since the COVID-19 outbreak, change of domestic work responsibilities since the COVID-19 outbreak on studies, COVID-19 pandemic affect on access to these research tools which inlude: Computer or laptop, Reliable Internet, Assistive Technology, Laboratory equipment, University Library, Archives/special collections and Access to patients/research participants. It als collected information on: any benefits to COVID-19 pandemic for your work, some ways one thinks their supervisor or line manager could support or help one manage the impacts of COVID-19 on studies

The questionnaire was developed in English and was latertranslated into French to accommodate the French speaking countries i.e Burundi and Rwanda. The French questionnaire was backtlanslated to English to ensure the questions still maintained their original meaning. This work was done by an external consultant and the French questionnaires were reviewed by the research assistant from Burundi and tested among postgraduate students in Light University.

All questionnares and modules are provided as external resources.

Cleaning operations

Qualitative The data was collected through qualitative interviews (In-depth interviews) and focus group discussions. They were audio recorded and the recordings were transcribed on Ms Ofiice.The transcript were subjected to data quality checks and the clean transcripts were anonyzed for data protection.

QUANTITATIVE Secondary data The data was collected from the five countries in an Ms Excel designed data abstraction sheet. The data abstraction sheet helped the universities administrators and rergistrars to directly enter the data only in the required field and for the defined or specific variables. For the dataset that was in hardcopy format the data entry was also done using the data abstraction sheets. The data sets were subjected to data quality checks for data quality. We used a standard template to ensure data editing took place during data entry.

Online survey Data entry was in form of responding to the survey. Data editing was done while cleaning the data.

Response rate

Quantitaive The online survey link was circulated using contacts within universities and research institutions in East Africa via email and social media platforms such as WhatApp hence it is impossible to track those who received the survey and hence it is not possible t calculate the survey response rate.

Sampling error estimates

NA

Algebra equation solving performance using hands-on equations for students with learning disabilities and their peers (grades 6-8)

Dataset DOI: 10.5061/dryad.sn02v6xh8

File list

• Algebra_Equation_Solving_Performance_Using_Hands-On_Equations_Manipulatives_(Grades_6-8).csv
• LD_Study_SAV_File.sav
• Borenson_LD_Study_Output_File.pdf

File descriptions

Algebra_Equation_Solving_Performance_Using_Hands-On_Equations_Manipulatives_(Grades_6-8).csv– the data in unformatted form.

LD_Study_SAV_File.sav– the data for direct use in SPSS.

Borenson_LD_Study_Output_File.pdf- results from statistical analysis (see Output file description under Usage Notes for further information).

Usage Notes

Datafile Description

• Variables/Columns:
  • Nr: Anonymized unique identifier for each student.
  • pretest: Score (0-6 points) on the initial algebra assessment administered before instruction.
  • posttestM: Score (0-6 points) on the...

Abstract

This study examines the effect of the use of two Open Educational Resources (OER) (a Khan Academy online tutorial and an open textbook hosted on Wikibooks) on logical-mathematical outcomes for first and second-year students in higher education institutions in Chile. It also investigates perceptions of instructors and students about the use of OER, in order to understand how these resources are used and valued. Quantitative and qualitative methods were used to collect student performance data via a student survey, student focus groups, interviews with instructors, and sourcing institutional records.

Only the institutional records, focus group data and interview data are included in the final dataset. Student survey data is not made available for confidentiality reasons. Findings indicate that students in a contact-study mathematics course who used a Khan Academy online mathematics tutorial obtained better examination results than students who did not use any additional resources, or those who used the open textbook. Moreover, it was also found that instructors and students have positive perceptions about the use of Khan Academy and Wikibooks materials.This study is Sub-project 9 of the Research on Open Educational Resources for Development (ROER4D) project, hosted by the Centre for Innovation in Learning and Teaching (CILT) at the University of Cape Town, South Africa, and Wawasan Open University, Malaysia.

Geographic coverage

The interviews and survey data were conducted at one institution in Chile and are not representative of the country as a whole.

Analysis unit

Individuals

Universe

The survey covered students and instructors in the single institution involved in the study.

Kind of data

Focus group and survey data

Mode of data collection

Face-to-face and internet [f2f-int]

Problem Statement

Topic Modeling for Research Articles Researchers have access to large online archives of scientific articles. As a consequence, finding relevant articles has become more difficult. Tagging or topic modelling provides a way to give token of identification to research articles which facilitates recommendation and search process.

Given the abstract and title for a set of research articles, predict the topics for each article included in the test set.

Note that a research article can possibly have more than 1 topic. The research article abstracts and titles are sourced from the following 6 topics:

1. Computer Science

2. Physics

3. Mathematics

4. Statistics

5. Quantitative Biology

6. Quantitative Finance

Data Dictionary

train.csv

Evaluation Metric

• Submissions are evaluated on micro F1 Score between the predicted and observed topics for each article in the test set.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

The database includes wire diameter measurement data, surface pattern morphology images, and fracture surface profile curves from the titled paper.

Electronic archive and distribution server for research articles providing open access to more than 850,000 e-prints in Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance and Statistics. Users can retrieve papers via the web interface. Registered authors may use the web interface to submit their articles to arXiv. Authors can also update their submissions if they choose, though previous versions remain available. Listings of newly submitted articles in areas of interest are available via the web interface, via RSS feeds, and by subscription to automatic email alerts.

Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The ‘communities’ of questionnaire items that emerge from our community detection method form possible ‘functional constructs’ inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such ‘functional constructs’ suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.