• Aucun résultat trouvé

Building up students’ data analytics skills to solve real world problems

N/A
N/A
Info
Télécharger
Protected

Academic year: 2021

Partager "Building up students’ data analytics skills to solve real world problems"

Copied!
3
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

HAL Id: hal-02429060

https://hal.archives-ouvertes.fr/hal-02429060

Submitted on 6 Jan 2020

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Building up students’ data analytics skills to solve real world problems

Sibel Kazak, Taro Fujita, Manoli Turmo

To cite this version:

Sibel Kazak, Taro Fujita, Manoli Turmo. Building up students’ data analytics skills to solve real world problems. Eleventh Congress of the European Society for Research in Mathematics Education, Utrecht University, Feb 2019, Utrecht, Netherlands. �hal-02429060�

(2)

Building up students’ data analytics skills to solve real world problems

Sibel Kazak1, Taro Fujita2 and Manoli Pifarre Turmo3

1Pamukkale University, Turkey; skazak@pau.edu.tr

2University of Exeter, UK; T.Fujita@exeter.ac.uk

3University of Lleida, Spain; pifarre@pip.udl.cat

Keywords: Data analytics, technology, informal statistical inference, K-12 education.

Theoretical background

In today’s age of information, data is very powerful in making informed decisions. Data analytics, which is a relatively new field, refers to processes used in identifying or discovering the trends and patterns inherent in the data to get useful insights for making data driven decisions (Piccano, 2012).

The Strategic Partnership for the Innovative Application of Data Analytics in Schools (SPIDAS) project (funded by Erasmus+ Programme) aims at developing students’ data analytics skills at various school levels (from primary to secondary) using innovative and student-centered approach.

In the context of SPIDAS project, we define DA as a process of 'engaging creatively in exploring data, including big data, to understand our world better, to draw conclusions, to make decisions and predictions, and to critically evaluate present/future courses of actions'. Our conceptual framework for the teaching and learning of data analytics in schools consists of two components: data analytics cycle and competence areas. Data analytics cycle is drawn on PPDAC (Problem, Plan, Data, Analysis, Conclusion) statistical inquiry cycle (Wild & Pfannkuch, 1999), statistical thinking process (Wild, Utts & Horton, 2011) and informal statistical inference (Makar & Rubin, 2009, 2018). This cycle involves the following steps in solving real world problems that require data: 1) defining the problem 2) considering data 3) exploring data 4) drawing conclusions 5) making decisions and 6) evaluating courses of actions. Additionally, the data analytics cycle is complemented with various competence areas that are in line with "Framework for 21st Century Learning" (http://www.p21.org/about-us/p21- framework) and the Royal Society’s (2016) report on the need for data analytics skills. These are statistical literacy, ICT literacy, critical thinking, creativity, communication and collaboration, ethics and social impact.

The study

This study aims to innovate and extend best practice in the teaching of data analytics through student- centered, project-based learning, focusing on the impacts of weather. The project end-product will be the Data Analytics Toolkit, an on-line resource supporting schools across the EU to develop their data analytics projects building on our examination of the state-of the-art and findings emerging from the pilot projects conducted in partnering schools in the UK, Spain and Turkey (http://blogs.exeter.ac.uk/spidasatexeter). To this end, a design study method (Cobb et al., 2003) is used for developing, testing and revising conjectures about how students develop skills and competencies related to data analytics and instructional materials to support it with the use of technology tools. The design of the pilot projects are guided by our data analytics framework. They

(3)

are implemented in nine partnering schools with 9–17 year-old students across three countries over two iterations (fall 2018 and spring 2019). One of the project themes to engage 9–12 year-old students in data analytics for solving real world problems is “How does weather affect our health and emotions?” through which we expect them to correlate weather data with other datasets, such as students’ feelings. In the first pilot study, students generated statistical questions and collected their own data from other students as well as obtained them from other sources, such as the meteorology office. Technology tools, e.g. Excel and CODAP (https://codap.concord.org/), were used to record, visualize and analyze their data to make decisions. Students’ work on computer and worksheets and classroom observations are analyzed to elaborate on how students interact with ill-structured real world problems to apply data analytics techniques and skills.

Poster presentation

The poster presents an overview of the conceptual framework for the teaching and learning of data analytics in schools, objectives and context of the study, task design and preliminary findings regarding how students deal with multivariate datasets to investigate different possible relationships in solving real world problems using data analytics techniques and skills.

The Strategic Partnership for the Innovative Application of Data Analytics in Schools (SPIDAS) project is funded with support from the European Union’s Erasmus+ Programme. All views expressed are those of the authors and not of the European Commission.

References

Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Shauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13.

Makar, K. & Rubin, A. (2009). A framework for thinking about informal statistical inference.

Statistics Education Research Journal, 8(1), 82–105. [Online: http://iase- web.org/documents/SERJ/SERJ8(1)_Makar_Rubin.pdf]

Makar, K., & Rubin, A. (2018). Learning about statistical inference. In D. Ben-Zvi, K. Makar and J.

Garfield (Eds.) International Handbook of Research in Statistics Education (pp. 261–294).

Springer International Handbooks of Education. DOI 10.1007/978-3-319-66195-7

Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9–20.

Royal Society (2016). Data analytics: the skills need in STEM. Conference report organised in partnership with the Royal Statistical Society held on 16 November 2016.

Wild, C. J. & Pfannkuch, M. (1999). Statistical thinking in empirical inquiry. International Statistical Review, 67(3), 223–248.

Wild, C. J., Utts, J. M., & Horton, N. J. (2011). What is statistics? In D. Ben-Zvi, K. Makar and J.

Garfield (Eds.) International Handbook of Research in Statistics Education (pp. 5–36). Springer International Handbooks of Education. DOI 10.1007/978-3-319-66195-7

Références

Télécharger maintenant ( PDF - 3 Page - 265.87 KB )

Documents relatifs

Data Models in Learning Analytics

These data can come from (but not limited to): centralized educational systems, distributed learning environments, open data sets, personal learning environments, adaptive

Challenges from Industrial Data Analytics

Michael was responsi- ble for a number of National and European funded research projects in the area of Data Mining, Machine Learning, and Big Data. Distribution of this paper

Contextualizing Geometric Data Analysis and Related Data Analytics: A Virtual Microscope for Big Data Analytics

We note once again, that this analysis is carried out on the 9 socio-demographic questions at issue here, and as 14 supplementary questions, these were questions related to

Big Data and Visual Analytics for Building Performance Comparison

Building’s behavior is comprised by a large number of heterogeneous information (e.g. geometric information, occupancy, energy consumption, etc.), which is very difficult to be

Collective Data Analytics Capability Building Processes: a Governance Model

In this paper, building on insights from the governnace literature and by using a case study from the customs domain we developed a governance model to support the analysis

Developing Big Data Analytics Architecture for Spatial Data

The big data analytics architecture [26] is built and implemented on big data open source technologies for the enrichment of massive scale data including spatial data.. The

Towards Linked Statistical Data Analysis

In this article, the design and implementation of an application and service is presented, which utilizes federated SPARQL queries to gather statistical data from distributed data

Real-Time Big Data Stream Analytics

Big Data is a new term used to identify the datasets that due to their large size, we can not manage them with the typical data mining software tools.. Instead of defining “Big Data”