Call for Papers Thematic issue on "Learning and Teaching Statistics: new challenges and frontiers"
In the era of data, Statistical literacy has become a fundamental skill that gives anyone a competitive edge in any field. Even in non-STEM (Science, Technology, Engineering, and Mathematics) graduate and undergraduate programs, primary or intermediate statistical knowledge is required even more. Statistics is offered in non-STEM undergraduate and master programs in Cultural Heritage, Health Sciences, Medicine, Political Science, Psychology, Social Science, and several others. However, students do not have a solid mathematical background in such courses, and teaching statistics must be utterly different concerning the STEM courses.
Many scientific articles highlighted that several reasons generally contribute to students' low performance in Statistics in human sciences courses. These reasons can be roughly gathered in three groups: (i) lack of background knowledge; (ii) psychological traits of anxiety towards mathematics and statistics; (iii) lack of motivation for the study of Statistics.
In this broad framework, the thematic issue is aimed at collecting contributions focused on statistical new methods and tools to teach statistics that are based on most recent IT innovations, as well as to any approach to assessing the students' statistical literacy, knowledge, and ability in applying knowledge, including their judgment abilities in problems that require any statistical method to be solved. Using knowledge and judgment abilities tend to make experience anxiety in the students (namely statistical anxiety). Psychometrics scales, instruments, and models aiming to measure statistical anxiety also fall in the scope of the issue.
Possible topics are:
- Statistical Models in Education
- New Frontiers in Teaching Statistics
- Environments for Self-Learning Statistics based on IT New Solutions
- Assessing Statistical Knowledge
Any other topic related to the issue's general aim can be favorably considered, upon request of the author(s).
All submitted manuscripts will be peer reviewed, and they are expected to adhere to the standards of the journal. Authors are encouraged to follow the specifications and use the templates (MS-Word or LaTeX) available in “Authors Guidelines” (see here).
During the submission procedure, specify in the field "Comments for the editor": Thematic issue on Learning and Teaching Statistics: new challenges and frontiers.
The deadline for paper submissions is 31 January 2022 31 March 2022 (extended deadline).
- Rosa Baños (University of Valencia, Spain)
- Raffaele Di Fuccio (Smartred Srl, Italy)
- Angelos Markos (Democritus University of Thrace, Greece)
- Domenico Vistocco (University of Naples Federico II, Italy)
- Adalbert F.X. Wilhelm (Jacobs University, Germany)
The issue is edited by the ALEAS partnerships. ALEAS stands for Adaptive Learning in Statistics and refers to the homonym ERAMSUS+ KA2 2018 agreement (https://aleas-project.eu) involving five partners from four European countries.