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Data about teamwork skill and knowledge acquisition through an interdisciplinary approach in a realistic context between Education and CS students in a HCI course

e-cienciaDatos, Repositorio de Datos del Consorcio Madroño
  • Maximiliano Paredes Velasco
  • Mónica Arnal Palacián
  • Jaime Urquiza Fuentes
  • Mercedes Martín Lope
The dataset is stored in one spreadsheet file with two sheets. The sheet named "Teamwork" contains data regarding the measurement of the teamwork soft skill acquisition. The sheet named "Knowledge" contains data regarding the measurement of the knowledge acquisition about human-computer interaction.

-Columns of the sheet "Teamwork"-

Timestamp_PRE. Timestamp of the pretest.
CodGroup. Three possible values: Early childhood education degree (1), Primary education degree (2), and Computer science (3)
Gender. Two possible values: Male(M) and female (F).
Age. Age range: 18, 19-20, 21-22, 23-24, and 25-30
AcadYear. Academic year of the degree where the student is enrolled: First (1), second (2), second (3), and fourth (4)
WORK_PRE. Completing assigned tasks within the deadline as group member. Measured in the prestest. Being 1 the worst case and 5 the best case.
PARTI_PRE. Participating actively in team meetings, sharing information, knowledge, and experiences. Measured in the prestest. Being 1 the worst case and 5 the best case.
ORGA_PRE. Collaborating in defining, organizing, and distributing group tasks. Measured in the prestest. Being 1 the worst case and 5 the best case.
COHE_PRE. Focusing on and being committed to agreement and shared objectives. Measured in the prestest. Being 1 the worst case and 5 the best case.
SOCIAL_PRE. Social value of activity, taking into account the points of view of others and giving constructive feedback. Measured in the prestest. Being 1 the worst case and 5 the best case.
TEAMW_PRE. Aggregates the five previous columns. Being 1 the worst case and 5 the best case.
Timestamp_POS. Timestamp of the postest
WORK_POS. Completing assigned tasks within the deadline as group member. Measured in the postest. Being 1 the worst case and 5 the best case.
PARTI_POS. Participating actively in team meetings, sharing information, knowledge, and experiences. Being 1 the worst case and 5 the best case.Measured in the postest.
ORGA_POS. Collaborating in defining, organizing, and distributing group tasks. Measured in the postest. Being 1 the worst case and 5 the best case.
COHE_POS. Focusing on and being committed to agreement and shared objectives. Measured in the postest. Being 1 the worst case and 5 the best case.
SOCIAL_POS. Social value of activity, taking into account the points of view of others and giving constructive feedback. Measured in the postest. Being 1 the worst case and 5 the best case.
TEAMW_POS. Aggregates the five previous columns. Being 1 the worst case and 5 the best case.
KNOWLEDGE. Knowledge acquisition regarding human-computer interaction concepts

-Columns of the sheet "Knowledge"-

Group. Two possible values: Control group (0) and experimental group (1)
Know. Measurement of the knowledge acquisition as a numeric value in the range [0-10], being 0 the worst case and 10 the best case.




Learning and Motivational Impact of Game-Based Learning: Comparing Face-to-Face and Online Formats on Computer Science Education

Archivo Digital UPM
  • López Fernández, Daniel
  • Gordillo Mendez, Aldo
  • Pérez Benedí, Jennifer
  • Tovar Caro, Edmundo
Contribution: This article analyzes the learning and motivational impact of teacher-authored educational video games on computer science education and compares its effectiveness in both face-to-face and online (remote) formats. This work presents comparative data and findings obtained from 217 students who played the game in a face-to-face format (control group) and 104 students who played the game in an online format (experimental group). Background: Serious video games have been proven effective at computer science education, however, it is still unknown whether the effectiveness of these games is the same regardless of their format, face-to-face or online. Moreover, the usage of games created through authoring tools has barely been explored. Research Questions: Are teacher-authored educational video games effective in terms of learning and motivation for computer science students? Does the effectiveness of teacher-authored educational video games depend on whether they are used in a face-to-face or online format? Methodology: A quasi-experiment has been conducted by using three instruments (pre-test, post-test, and questionnaire) with the purpose of comparing the effectiveness of game-based learning in face-to-face and online formats. A total of 321 computer science students played a teacher-authored educational video game aimed to learn about software design. Findings: The results reveal that teacher-authored educational video games are highly effective in terms of knowledge acquisition and motivation both in face-to-face and online formats. The results also show that some students’ perceptions were more positive when a face-to-face format was used.




Enhancing Web Applications Observability through Instrumented Automated Browsers

Archivo Digital UPM
  • García Gutiérrez, Boni
  • Ricca, Filippo
  • Álamo Ramiro, José María del
  • Leotta, Maurizio
In software engineering, observability is the ability to determine the current state of a software system based on its external outputs or signals such as metrics, logs, or traces. Web engineers rely on the web browser console as the primary tool to monitor the client-side of web applications during end-to-end tests. However, this is a manual and time-consuming task due to the different browsers available. This paper presents BrowserWatcher, an open-source browser extension providing cross-browser capabilities to observe web applications and automatically gather browser console logs in different browsers (e.g., Chrome, Firefox, or Edge). We have leveraged this extension to conduct an empirical study analyzing the browser console of the top-50 public websites manually and automatically. The results show that BrowserWatcher gathers all the well-known log categories such as console or error traces. It also reveals that each web browser additionally includes other types of logs, which differ among browsers, thus providing distinct pieces of information for the same website.