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.

This article describes a free, open-source collection of templates for the popular Excel (2013, and later versions) spreadsheet program. These templates are spreadsheet files that allow easy and intuitive learning and the implementation of practical examples concerning descriptive statistics, random variables, confidence intervals, and hypothesis testing. Although they are designed to be used with Excel, they can also be employed with other free spreadsheet programs (changing some particular formulas). Moreover, we exploit some possibilities of the ActiveX controls of the Excel Developer Menu to perform interactive Gaussian density charts. Finally, it is important to note that they can be often embedded in a web page, so it is not necessary to employ Excel software for their use. These templates have been designed as a useful tool to teach basic statistics and to carry out data analysis even when the students are not familiar with Excel. Additionally, they can be used as a complement to other analytical software packages. They aim to assist students in learning statistics, within an intuitive working environment. Supplementary materials with the Excel templates are available online.

The Flexible Sheet-to-Sheet Vertical Continuous Electroplating Equipment market is a crucial segment of the electroplating industry, widely utilized in manufacturing sectors that require high-quality surface finishes on metal products. This equipment allows for the seamless electroplating of flexible sheets in a ver

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The inventory volume of fabricated pneumatic tools in the fabricated metals industry in Japan decreased by 4.8 thousand units (-6.06 percent) since the previous year. In 2023, the inventory quantity thereby reached its lowest value in the recent years. Find more statistics on other topics about Japan with key insights such as production quantity of fabricated pneumatic tools, sales quantity of wrenches and spanners, and sales value of shear blades made from steel sheets.

The global Building and Construction Sheets Market is expected to rise USD 279.20 billion by 2034 And anticipated to grow at a CAGR of 5.3%.

This dissertation builds upon a systematic literature review conducted by my supervisors (Topali, P., Ortega-Arranz, A., Rodríguez-Trianaet al. Designing human-centered learning analytics and artificial intelligence in education solutions: a systematic literature review. 2024), which focused on empirical studies involving the design, development, and implementation of human-centered learning analytics and artificial intelligence tools in education. From the articles included in that review, a subset of relevant tools was identified through an initial screening process. These tools were then analyzed to better understand their features and provide stakeholders with a structured overview of available systems that align with human-centered principles in Learning Analytics.

The methodology adopted for this work was inspired by DESMET, enabling a systematic and structured evaluation of tool characteristics. The process involved iterative reading of associated literature, hands-on exploration of the tools, and direct communication with the authors or developers to validate the extracted information. A deductive, top-down classification approach was initially used to define broad categories covering both qualitative and quantitative aspects of the tools. These categories were progressively refined through multiple iterations, following an inductive, bottom-up strategy to enhance internal coherence and thematic clarity.

Two rounds of outreach were conducted to validate the collected data, with several authors providing valuable feedback that was incorporated into the final analysis. The outcome is a categorised, feature-based overview of HCLA and HCAI tools, offering practical insights for researchers and practitioners seeking to adopt or further explore human-centered approaches in educational technology.

Keywords: Human-Centered Learning Analytics, Human-Centered Artificial Intelligence, Learning Analytics, Human-Centered Design, DESMET, Systematic Analysis, Feature Analysis

We defined a number of categories that capture the most important characteristics of each tool. For clarity, we organised the categories into five main groups:

Tool Basis - Includes fundamental information about the tool, such as its name, purpose, pedagogical context, and technological aspects.
Human-Centered Approach - Focuses on how stakeholders (e.g., students, teachers, researchers) are involved in the tool's development, use, and feedback processes.
Data Management - Covers aspects related to data collection, storage, processing, and privacy considerations.
Tool Evaluation - Examines how the tool's effectiveness, usability, and impact are assessed.
Tool Adoption - Explores the extent to which the tool is used, its sustainability, and its integration into educational settings.

Te data is organized into four groups of tabs, each one is formed by the 5 groups mentioned before, to facilitate the management and analysis of the information collected
during the methodological process (20 in total).
1. The first 5 sheets, contain the information collected directly by myself after completing
the iterations described in the methodology process; that is, the raw data.
2. The next 5, include tabs with comments and feedback provided by the authors.
3. The next 5, comprise tabs where the authors’ contributions have been integrated
into the original data mentioned in point 1, thus combining both sources.
4. The last 5 contain normalized or standardized data prepared for the
quantitative and qualitative analyses carried out in this study.

United Kingdom British Airways: Asset: OE: Property & Other Equipment data was reported at 2,312.000 GBP mn in 2014. This records an increase from the previous number of 2,203.200 GBP mn for 2013. United Kingdom British Airways: Asset: OE: Property & Other Equipment data is updated yearly, averaging 2,151.400 GBP mn from Dec 2000 (Median) to 2014, with 15 observations. The data reached an all-time high of 2,403.400 GBP mn in 2001 and a record low of 1,998.500 GBP mn in 2006. United Kingdom British Airways: Asset: OE: Property & Other Equipment data remains active status in CEIC and is reported by Civil Aviation Authority. The data is categorized under Global Database’s United Kingdom – Table UK.TA009: Airline Financial Statistics: Balance Sheet: British Airways.

United Kingdom British Airways: Asset: OE: Property & Other Equipment: Net data was reported at 999.800 GBP mn in 2014. This records an increase from the previous number of 969.400 GBP mn for 2013. United Kingdom British Airways: Asset: OE: Property & Other Equipment: Net data is updated yearly, averaging 1,178.300 GBP mn from Dec 2000 (Median) to 2014, with 15 observations. The data reached an all-time high of 1,714.700 GBP mn in 2000 and a record low of 969.400 GBP mn in 2013. United Kingdom British Airways: Asset: OE: Property & Other Equipment: Net data remains active status in CEIC and is reported by Civil Aviation Authority. The data is categorized under Global Database’s United Kingdom – Table UK.TA009: Airline Financial Statistics: Balance Sheet: British Airways.

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In the most recently reported fiscal year, Google's revenue amounted to 348.16 billion U.S. dollars. Google's revenue is largely made up by advertising revenue, which amounted to 264.59 billion U.S. dollars in 2024. As of October 2024, parent company Alphabet ranked first among worldwide internet companies, with a market capitalization of 2,02 billion U.S. dollars. Google’s revenue Founded in 1998, Google is a multinational internet service corporation headquartered in California, United States. Initially conceptualized as a web search engine based on a PageRank algorithm, Google now offers a multitude of desktop, mobile and online products. Google Search remains the company’s core web-based product along with advertising services, communication and publishing tools, development and statistical tools as well as map-related products. Google is also the producer of the mobile operating system Android, Chrome OS, Google TV as well as desktop and mobile applications such as the internet browser Google Chrome or mobile web applications based on pre-existing Google products. Recently, Google has also been developing selected pieces of hardware which ranges from the Nexus series of mobile devices to smart home devices and driverless cars. Due to its immense scale, Google also offers a crisis response service covering disasters, turmoil and emergencies, as well as an open source missing person finder in times of disaster. Despite the vast scope of Google products, the company still collects the majority of its revenue through online advertising on Google Site and Google network websites. Other revenues are generated via product licensing and most recently, digital content and mobile apps via the Google Play Store, a distribution platform for digital content. As of September 2020, some of the highest-grossing Android apps worldwide included mobile games such as Candy Crush Saga, Pokemon Go, and Coin Master.

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It includes local authority, regional and national data on:

• children looked after and adoption
• child protection
• the children’s social care workforce
• special educational needs and disability
• pupil attainment and attendance
• children’s health
• youth justice
• post-16
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The ‘Children’s services statistical neighbour benchmarking tool’ allows you to select a local authority and display its ‘closest statistical neighbours’ (local authorities with similar characteristics). The tool has been reviewed and rebuilt to include updated socio-economic variables from the 2021 census. More information is available in the associated update note and technical report.

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