Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Data Notes

• Class size audits are conducted by CESE (Centre for Education Statistics and Evaluation) in March each year. Audits were not conducted in 1998, 1999, 2000 and 2001.

• Data for 2020 should be treated with caution. The collection took place in March when schools were impacted by COVID-19, so fewer data checks were carried out.

• Students attending schools for specific purposes (SSPs), students in support classes in regular schools and distance education students are excluded from average class size calculations.

• The average class size for each grade is calculated by taking the number of students in all classes that a student from that grade is in (including composite/multi age classes) divided by the total number of classes that includes a student from that grade. This can result in a lower Kindergarten to Year 6 average class size than any individual year level.

• From 2017, school size is based on primary enrolment rather than school classification.

• Schools change size, so data in Table 2 is not necessarily comparable to previous iterations in earlier fact sheets.

Data Source

Education Statistics and Measurement, Centre for Education Statistics and Evaluation.

Data quality statement

The Class Size Audit Data Quality Statement addresses the quality of the Class Size Audit dataset using the dimensions outlined in the NSW Department of Education's data quality management framework: institutional environment, relevance, timeliness, accuracy, coherence, interpretability and accessibility. It provides an overview of the dataset's quality and highlights any known data quality issues.

As more university instructors continue making recordings of in-person classes available, educators should carefully consider how modality options may affect learners. While most existing studies that compare learning modalities rely on survey studies and broader correlations, we conducted an interventional qualitative study to glean more theory about how students experience different modalities. Nine students enrolled in a large introduction to biostatistics course volunteered to participate. For two different 50-min class periods, participating students were randomly assigned to do one of the following: attend class in person, watch the class recording, or watch prerecorded videos made by the instructor. Interviews revealed that students’ ability to self-regulate their learning was a key indicator of whether they could learn richly and successfully with video-based modalities. In-person class attendance had value for several, but typically as a vehicle for maintaining discipline and good habits rather than as an opportunity to learn more richly. We theorize that developing students’ ability to plan, monitor, and evaluate their own learning processes plays a crucial role in their success across multiple modalities. Furthermore, supporting students to notice and focus on conceptual ideas in statistics may better support reflective learning in courses where class recordings are available.

Tokyo is the city where the highest number of consumers counts as middle class and above. In the Japanese capital, ** million people earned at least the equivalent of the highest ** percent of global income earners as of 2022 in purchasing power parity (PPP) terms. Delhi and Shanghai followed behind.

Education and further studies: refers to various learning, education and related information collections.

Description: In the year 2023, the Central Board of Secondary Education (CBSE) conducted the Class XI examinations across various regions. The dataset presents a comprehensive overview of the results, categorized by different types of schools and regions. The data includes the number of students registered, the number of students who appeared for the exams, and the performance status of each category.

The results encompass a diverse range of schools, including those under the Central Tibetan School Administration (CTSA), Jawahar Navodaya Vidyalayas (JNV), and Kendriya Vidyalayas (KV), as well as government and government-aided schools, and independent institutions.

The "Status" column provides insights into the outcome of the exams, highlighting the number of students who successfully cleared the examinations. The "Region" column denotes the geographic distribution of the schools, allowing for a comprehensive analysis of performance across different areas.

The dataset is a valuable resource for understanding the educational landscape and performance trends within the CBSE Class XI examinations for the year 2023. It offers an in-depth view of student participation, success rates, and the performance of different types of schools across various regions, contributing to a holistic assessment of the CBSE educational system's effectiveness and impact. Researchers, educators, and policymakers can leverage this data to identify patterns, make informed decisions, and implement targeted interventions to enhance the overall quality of education.

Hogs and pigs statistics, inventory number by class and semi-annual period, United States (head x 1,000). Data are available on a semi-annual basis.

This statistic shows the number of China's middle class population in 2002 and a forecast for 2020. According to the forecast, the middle class in China would grow to approximately *** million by 2020.

Between November 2023 to November 2024, approximately *** million people participated in fitness classes in England. This marked an increase on the previous survey period.

This national level table contains the number of postgraduate new entrants for each ITT route and subject by degree class. The data in this table covers 2019/20 to 2024/25 (2024/25 is provisional, all previous years are revised).

This table contains 9 series, with data for years 2001 - 2012 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: United States and Canada); Livestock (9 items: All hogs and pigs;Hogs and pigs kept for breeding;Market hogs and pigs;Under 23 kilograms; ...).

Electronic nicotine delivery systems (ENDS), such as e-cigarettes, have become increasingly used across the world. To respond to global public health challenges associated with vaping, governments have implemented numerous ENDS policies. This research highlights the patterns, clustering, and transitions in U.S. state ENDS policy implementation from 2010 to 2020. Policy data for tobacco and ENDS policies primarily from the Americans for Nonsmokers’ Rights Foundation (ANRF) were analyzed for the years 2010 to 2020 for all fifty states and Washington, D.C. Patterns and clusters of policies were assessed. Latent trajectories were modeled for ENDS policies across states over time. ENDS policies commonly have analogous tobacco control policies in place prior to their implementation. ENDS policies in states were commonly implemented in “bundles.” The temporal trajectories of ENDS policy implementation occurred in 3 latent forms. A majority of states were “catch-up implementers,” indicating their slow initial implementation but stronger position by the end of the period of observation in 2020. These trajectories of ENDS policies were not associated with any individual tobacco control policy in place at the start of the trajectory in 2010. The development of ENDS policies in U.S. states has been temporally and geographically uneven. Many states that had initially been slow to implement ENDS policies caught up by 2020. The implementation of policy “bundles” was common. The clustering of policies in bundles has important methodological implications for analyses, which should be considered in ENDS policy evaluations.

This dataset presents statistics on the number and total sales, value of shipments, or revenue of establishments; distribution of sales, shipments, or revenue by class of customer; and sales, shipments, or revenue of establishments responding to class of customer inquiry as a percent of total revenue for selected industries for selected geographies. Includes only establishments of firms with paid employees.

Participant demographics and summary statistics.

Key Online Course App StatisticsTop Online Course AppsEducation App Market LandscapeOnline Course App RevenueOnline Course Revenue by AppOnline Course App UsersOnline Course Users by AppOnline Course...

Data for: Class-Divided Cities, Detroit Edition Published in Atlantic Cities, April 10 2013 http://www.theatlanticcities.com/neighborhoods/2013/04/class-divided-cities-detroit-edition/4679/