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This tool focuses on tackling sexual violence, hate crime and online harassment, and is designed for higher education institutions to self-review their online safeguarding practice.

This tool defines 23 features of related policy and practice around online safeguarding for higher education institutions. Each feature can be self-assessed at four levels, graded from 0 to 3. The tool provides clear definitions for 23 features and levels related to online safeguarding. For each feature a level can be determined by reading the level descriptions and deciding which one fits your own institutional practice most closely. Or, for a feature where institutional practice does not meet the definition for level 1, you can score that feature as level 0. Once you have defined baseline policy and practice at your institution you can use the tool to inform the  development of an improvement plan, which can be regularly reviewed as policy and practice improves. The definitions for higher levels in each feature give clear guidance on how to enhance online safeguarding practice. The tool and improvement plan can be updated as policy and practice in your institution develops.

The Evaluation Starter Kit course supports teachers who want to learn about using evidence to evaluate programs and approaches to enhance student wellbeing and learning outcomes.

 

The course provides you with practical tasks that build understanding and skills through 4 informative modules. This freeself-paced course:

• revises what you already know about evaluation, and allows you to assess your own confidence about conducting evaluations in your school context
• gives you the tools to design and conduct effective evaluations
• outlines how you can apply your learning to make evidence-informed decisions about programs for your school.

After completing Module 1 you will:
• understand the concept and importance of evaluation
• be able to identify the key principles of evaluation
• be able to describe the cycle of continuous evaluation for improvement.

After completing Module 2 you will be able to:
• select an issue, program or approach for evaluation
• apply the steps of evaluation to begin developing an evaluation plan.

After completing Module 3 you will:
• be able to explain the different approaches to evaluation and types of evaluation
• have an understanding of different data collection tools
• be able to select the right tool for your evaluation goals
• have access to templates and guidelines.

After completing Module 4 you will be able to:
• analyse and interpret data
• make evidence-informed decisions
• identify next steps
• apply your learning to assess a program or approach.

The fundamental objective of the study presented in this article is to formulate a theoretical model with an empirical base that identifies the factors associated with the well-being of teachers, when they tackle processes of educational innovation mediated by the use of the information and communication technologies (ICT).

Subjective well-being is an area of study of social psychology linked to the studies into “happiness” or “satisfaction with life” and constitutes an increasingly broad theoretical body. A questionnaire was produced, based on the scientific foundations that support the proposed model, and its validity and reliability have been established. The population and sample is made up of 322 teachers from non university centres that carry out innovative experiences with ICT in four Regions of Spain. The results obtained confirm five latent variables that explain the teacher well-being associated with innovation practices in ICT: (1) values/projects, (2) motivation, (3) competences, (4) satisfaction and (5) emotions. An explanatory structural model of teacher well-being is empirically validated.

The findings of the paper could be of interest in identifying and promoting the relevant keys that help to improve the emotional states of working teachers.

Suggestions on how education providers can ensure that their use of data to support wellbeing does not create risks for students or staff.

Whereas learning analytics uses data to inform decisions – from individual to curriculum level – on how to support students’ learning, data may also be used to inform decisions on how to support their wellbeing and mental health. Possible applications cover a very wide range: from screen-break reminders to alerts when a student appears to be at risk of suicide. Clearly such uses of data can involve both significant benefits and high risks.

This code of practice suggests how universities, colleges and other tertiary education providers can ensure that their use of data to support wellbeing does not create risks for students or staff, taking responsibility and demonstrating accountability for their actions in selecting, developing, implementing, operating and reviewing data-informed wellbeing processes.