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1. Primary Education Degree Programs in Alicante, Barcelona and Helsinki: Could the Differences in the Mathematical Knowledge of Incoming Students Be Explained by the Access Criteria?

Author

Gorgorió, Núria, Albarracín, Lluís, Laine, Anu, and Llinares, Salvador

Abstract

This perspective paper draws on the interest in ensuring that students who enter primary teacher training programs have a solid background knowledge of mathematics. We describe the access criteria and requirements for admission to the primary education degree programs at the Universidad de Alicante and Universitat Autònoma de Barcelona, in Spain, and the University of Helsinki, in Finland. We present the results of an evaluation of the mathematical knowledge that students bring to their education as teachers at these three institutions. The results show that in each program, the subgroup of students who had followed the longer track of mathematics courses scored significantly higher on the mathematical test, although this was no longer as clear when we compared across universities. We also found that the students who had taken the mathematics section of the entrance examination or the matriculation examination scored higher on the test than those from the same program who had not, but this tendency broke down when cross-university comparisons were made. We also explored how the cap set on the number of students admitted to the three programs -- this being the most striking difference in the admission policies -- could be an explanatory variable for these discrepancies. The comparison between universities leads us to hypothesize that expecting applicants to have met certain requirements in their academic trajectories prior to university entrance and adjusting the cap set on the number of places could ensure a better mastery of mathematical knowledge among those students admitted to the Spanish programs.

Published
2021

2. Proceedings of the International Association for Development of the Information Society (IADIS) International Conference on Cognition and Exploratory Learning in Digital Age (CELDA) (Madrid, Spain, October 19-21, 2012)

Author

International Association for Development of the Information Society (IADIS)

Abstract

The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a fast pace and affecting academia and professional practice in many ways. Paradigms such as just-in-time learning, constructivism, student-centered learning and collaborative approaches have emerged and are being supported by technological advancements such as simulations, virtual reality and multi-agents systems. These developments have created both opportunities and areas of serious concerns. This conference aimed to cover both technological as well as pedagogical issues related to these developments. The IADIS CELDA 2012 Conference received 98 submissions from more than 24 countries. Out of the papers submitted, 29 were accepted as full papers. In addition to the presentation of full papers, short papers and reflection papers, the conference also includes a keynote presentation from internationally distinguished researchers. Individual papers contain figures, tables, and references.

Published
2012

3. Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)

Author

International Working Group on Educational Data Mining, Barnes, Tiffany, Desmarais, Michel, Romero, Cristobal, and Ventura, Sebastian

Abstract

The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software and databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for using those data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This publication presents the following papers: (1) A Comparison of Student Skill Knowledge Estimates (Elizabeth Ayers, Rebecca Nugent, Nema Dean); (2) Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task (Ryan S.J.d. Baker); (3) A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials (Dror Ben-Naim, Michael Bain, and Nadine Marcus); (4) Detecting Symptoms of Low Performance Using Production Rules (Javier Bravo and Alvaro Ortigosa); (5) Predicting Students Drop Out: A Case Study (Gerben W. Dekker, Mykola Pechenizkiy and Jan M. Vleeshouwers); (6) Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning (Mingyu Feng, Joseph E. Beck and Neil T. Heffernan); (7) Does Self-Discipline impact students' knowledge and learning? (Yue Gong, Dovan Rai, Joseph E. Beck, and Neil T. Heffernan); (8) Consistency of Students' Pace in Online Learning (Arnon Hershkovitz and Rafi Nachmias); (9) Student Consistency and Implications for Feedback in Online Assessment Systems (Tara M. Madhyastha and Steven Tanimoto); (10) Edu-mining for Book Recommendation for Pupils (Ryo Nagata, Keigo Takeda, Koji Suda, Junichi Kakegawa, and Koichiro Morihiro); (11) Conditional Subspace Clustering of Skill Mastery: Identifying Skills that Separate Students (Rebecca Nugent, Elizabeth Ayers, and Nema Dean); (12) Determining the Significance of Item Order In Randomized Problem Sets (Zachary A. Pardos and Neil T. Heffernan); (13) Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models (Philip I. Pavlik Jr., Hao Cen, Kenneth R. Koedinger); (14) Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments (David Nadler Prata, Ryan S.J.d. Baker, Evandro d.B. Costa, Carolyn P. Rose, Yue Cui, Adriana M.J.B. de Carvalho); (15) Using Dirichlet priors to improve model parameter plausibility (Dovan Rai, Yue Gong, Joseph E. Beck); (16) Reducing the Knowledge Tracing Space (Steven Ritter, Thomas K. Harris, Tristan Nixon, Daniel Dickison, R. Charles Murray, and Brendon Towle); (17) Automatic Detection of Student Mental Models During Prior Knowledge Activation in MetaTutor (Vasile Rus, Mihai Lintean, and Roger Azevedo); (18) Automatic Concept Relationships Discovery for an Adaptive E-course (Marian Simko, Maria Bielikova); (19) Unsupervised MDP Value Selection for Automating ITS Capabilities (John Stamper and Tiffany Barnes); (20) Recommendation in Higher Education Using Data Mining Techniques (Cesar Vialardi, Javier Bravo Agapito, Leila Shafti, Alvaro and Ortigosa); (21) Developing an Argument Learning Environment Using Agent-Based ITS (ALES) (Safia Abbas and Hajime Sawamura); (22) A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks (Antonio R. Anaya and Jesus G. Boticario); (23) Dimensions of Difficulty in Translating Natural Language into First-Order Logic (Dave Barker-Plummer, Richard Cox, and Robert Dale); (24) Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems (Suleyman Cetintas, Luo Si, Yan Ping Xin, and Casey Hord); (25) Back to the future: a non-automated method of constructing transfer models (Ming Feng and Joseph Beck); (26) How do Students Organize Personal Information Spaces? (Sharon Hardof-Jaffe, Arnon Hershkovitz, Hama Abu-Kishk, Ofer Bergman, and Rafi Nachmias); (27) Improving Student Question Classification (Cecily Heiner and Joseph L. Zachary); (28) Why, What, and How to Log? Lessons from LISTEN (Jack Mostow and Joseph E. Beck); (29) Process Mining Online Assessment Data (Mykola Pechenizkiy, Nikola Trcka, Ekaterina Vasilyeva, Wil van der Aalst, and Paul De Bra); (30) Obtaining Rubric Weights For Assessments By More Than One Lecturer Using A Pairwise Learning Model (J. R. Quevedo and E. Montanes); (31) Collaborative Data Mining Tool for Education (Enrique Garcia, Cristobal Romero, Sebastian Ventura, Miguel Gea, and Carlos de Castro); (32) Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming (Amelia Zafra and Sebastian Ventura); and (33) Visualization of Differences in Data Measuring Mathematical Skills (Lukas Zoubek and Michal Burda). Individual papers contain tables, figures, footnotes, references and appendices.

Published
2009

4. Code to Learn: Where Does It Belong in the K-12 Curriculum?

Author

Moreno-León, Jesús, Robles, Gregorio, and Román-González, Marcos

Abstract

The introduction of computer programming in K-12 has become mainstream in the last years, as countries around the world are making coding part of their curriculum. Nevertheless, there is a lack of empirical studies that investigate how learning to program at an early age affects other school subjects. In this regard, this paper compares three quasi-experimental research designs conducted in three different schools (n = 129 students from 2nd and 6th grade), in order to assess the impact of introducing programming with Scratch at different stages and in several subjects. While both 6th grade experimental groups working with coding activities showed a statistically significant improvement in terms of academic performance, this was not the case in the 2nd grade classroom. Notable disparity was also found regarding the subject in which the programming activities were included, as in social studies the effect size was double that in mathematics.

Published
2016