Project Description

Description

The project will include three main actions (besides project management and dissemination) to strengthen young students’ ability to think critically and assess the trustworthiness of information accessed:

• adaptation and translation of “Elli’s World”;
• adaptation and translation of teachers’ professional development;
• educational data mining.

In the first part of the project, the partnership will meet to discuss the cultural adaptations of the two main project tools: the Elli’s World and the Teacher Professional Development Model.

In the second part of the project, we will adapt, test, and translate the two tools.

In the third part of the project, we will work on the identification of participants’ profiles through educational data mining.

• Elli’s World

The Elli’s World modified (EWm) represents a localised extension of the “original” The Elli’s World (EWo): a game-based intervention toolkit of self-regulation cognitive processes through gamification. EWo has been already developed and validated in Italy. It will be enhanced with:

a) cultural adaptations of the activities;

b) a new area dedicated to media and information literacy focused on the comprehension of digital sources;

c) a Finnish and English translated version.

To test the efficacy of EWm we will apply a cluster-randomized control trial. Classrooms involved will be randomly assigned to one of the experimental conditions: the EWm condition and an active control condition (that is, a condition in which the participants are engaged by external staff in educational activities that are not focused on self-regulation of cognitive processes).

We will identify valid and reliable measures to assess outcome and control variables. Collected data will be analyzed through linear mixed models to control for random effects due to classroom-level variables.

• Teacher Professional Development Model

In this action, we will develop and test the Teacher Professional Development Model for promoting one of the currently essential aspect of media literacy, that is critical information literacy, including credibility evaluation of online information. The model is based on teacher co-learning including acquisition of theoretical and pedagogical knowledge (including best practices), applying the knowledge in designing and implementing a teaching experiment in one’s own classroom, and systematic guided reflection. The Teacher Professional Development Model can be applied for independent teacher co-learning, or teachers’ learning process can be facilitated by a societal partner organizing teacher professional development. In the proposed project, the teacher professional development unit will include the following materials for teachers:

- Expert videos and podcasts on the following topics:

Key concepts of critical online information literacy, students’ critical online information literacy skills, best practices, and possibilities of gamification for teaching critical evaluation of online texts, students’ motivation in learning critical information literacy skills, and teaching of diverse learners.

- Selected readings

- Materials for instruction at school, e.g., exemplar tasks for students

- Guidelines to build a short teaching experiment in their classrooms

- Learning tasks for teachers

- Guided reflection tasks

- Self-efficacy questionnaire supporting reflection In the project, we will culturally adapt the TPDM materials to other contexts and translate it into Italian and English.

• Educational data mining

The data mining and modelling includes three activities.

A1. Exploratory data analysis (EDA). A comprehensive exploration of the available data collected prior, during, and after the intervention. This analysis will show what are the main predictors for performance, reveal new hypotheses to be tested, and pave the way for the development of the clustering analysis and learning path modelling. The EDA will reveal linear and nonlinear relations between variables and inform the selection for the class of models to be used for learning path modelling.

A2. Prior, during, and post intervention clustering. A K-means and hierarchical clustering approach will be used to group participants based on the structure and relationship between the measured variables. This analysis will reveal any significant differences between participants prior, during, and after the intervention. This analysis will highlight which are the main variables to use for distinguishing between the groups. Additionally, dimensionality reduction techniques (SVD, PCA) will also be employed to extract the most important features (measured variables) or their linear combination.

A3. Learning path model. The prior analysis lays the ground for the learning path model. A probabilistic state model will be developed to serve as the foundation for the analysis framework and as a basis for later studies and additional hypotheses.