Pplus_short_TA3_DIGI

Resources, Glossary

Mind Map

Case Study

Effective feedback and support students to develop their learning practice

Utilisation of digital technologies for diverse and appropriate assessments

Analysis and interpretation of learning evidences, assessment data and measurement results

Final Quiz - Survey - Badge

Preliminary Assessment

Sum up Infographic

Introduction of Module

Unit 3.2

Index

Supporting materials

Learning materials

Unit 3 .1

Unit 3.3

Testimonial

Assessment determines the entire educational process and is an integral part of the teaching-learning. Digital technologies and tools enrich modes and ways of assessment and can effectively support and improve learning. Let’s see what solutions are provided by digital assessment.

• In this comprehensive course we aim to equip teachers with the necessary skills and strategies to effectively embed digitalization techniques into their teaching practices, fostering a more digitally inclined approach among students.
• Throughout the course, you will explore a wide range of topics, including leveraging educational technology tools, designing engaging digital learning experiences, promoting digital literacy and developing digital skills, and nurturing critical thinking in a digital context.
• We will delve into practical methodologies for incorporating interactive multimedia content, online collaboration platforms, and personalized learning approaches.
• By the end of this course, you will have gained the knowledge and confidence to create dynamic digital learning environments that inspire and empower students to thrive in today's increasingly digital world.

Welcome to our innovative course Digitalization in Learning and Teaching

Intoduction Course

To provide effective feedback to students with the support of digital tools.

To develop follow-up steps to improve learning and teaching.

Learning outcomes of the module:

Keywords: assessment, digital assessment, digital technologies for assessment, learning evidence and data, learning analytics, effective feedback, self-regulated learning (SRL)

To develop assessment utilizing appropriate digital technologies and tools.

This module addresses teachers working within any field of study and proposes general tips on how to improve assessment by using digital solutions to enhance and improve teaching and learning. The module also provides practical exercises, a wide range of resources, and checklists for self-assessments.

To adequately interpret learning evidence and data.

In this module you will learn how to use digital technologies for the development of assessment, to analyse students’ performance and to give effective feedback so as to support learning.

Intoduction Module

Formative assessment

Summative assessment

Personalisation of learning

Diagnostic assessment

Digital assessment

Digital technologies and tools for assessment

Effective feedback

Digital tools for assessment

Learning evidence and data

Learning analytics

Self-regulated learning

AI for assessment

Mindmap of the topic

Self-reflection is a valuable tool when developing your course. It helps you identify your strengths and areas for improvement. Our course supports you in strengthening your weaknesses and designing a better learning experience for your students.

Assess yourself along the PROFFORMANCE Assessment tool statements DIGI TA3 - Competences on innovative teaching and learning

Assess your competence in Student’s learning assessment by the PROFFORMANCE Assessment Tool.

What is your experience?

Statements

Assessment Tool

Self-reflection

This is a paragraph of text waiting to be awesome content

I adapt learning practices and provide support according to the results generated using digital technologies.

I create, critically analyse, and interpret digital data on student’s activities, performance and progress.

I utilize digital technologies for diverse and appropriate formative and summative assessments.

I use digital technology to provide timely, targeted and clear feedback to students.

Hint

Hint

Hint

Hint

Assess yourself

Assess yourself

Assess yourself

Assess yourself

This is a paragraph of text waiting to be awesome content

UNIT 3.1

Utilisation of digital technologies for diverse and appropriate assessments

In this unit we will focus :

• What means digital assessment.
• Which kind of strategies we can use for digital assessment.
• Which factors we should be aware regarding digital assessment.

'Learners need endless feedback more than they need endless teaching.' - Grant Wiggins

Advantages of Digital Assessment:

Basic features of digital assessment:

Utilisation of digital technologies for diverse and appropriate assessments

Efficiency and Speed

Engagement

Personalized Learning

Environmentally Friendly

Data Analytics

Versatility

Flexibility and Accessibility

Technology-enabled assessment

Automated feedback and scoring

Data-driven insights

Accessibility and flexibility

UNIT 3.1

Watch

defines forms and types of feedback to provide differentiated and personlalised support

identifies a wide range of tools to monitor learner progress and evaluate achievements

identifies multiple modes of representation and demonstration of what students learned

sets a variety of assessment methods

is aligned with learning outcomes

sets clear goals and expectations of assessment and provides students with opportunities to self-assess, peer-assess, and receive feedback,

Assessment strategy

When designing assessment, starting with elaboration of an assessment strategy

Assessment strategies

UNIT 3.1

Summative Assessment Tools

Performance-Based Assessment Tools

Learning Analytics and Data Tools

Diagnostic Assessment Tools

Formative Assessment Tools

Incorporating a mix of these tools can create a robust and well-rounded assessment strategy that supports diverse and differentiated learning.

Selecting the right assessment tool depends on several factors:

• Type and purpose of assessment

UNIT 3.1

• Technology integration

• Feedback mechanisms

• Class size

• Discipline-specific needs

• Learning objectives

Selecting digital tools for different forms of assessment

Vanderbilt University

Dartmouth College

Columbia University

Below there are 3 universities’s assessment frameworks providing detailed guidance and practices for evaluating student learning.

University’s assessment framework

UNIT 3.1

7.

10.

4.

6.

1.

3.

5.

8.

9.

2.

Reflection exercise

UNIT 3.1

Selecting and Utilizing Digital Assessment Tools: Choosing the right digital tools is essential. Consider the assessment type and purpose, learning objectives, discipline-specific needs, class size, feedback mechanisms, and technology integration. A mix of tools, including formative, summative, diagnostic, and performance-based assessment tools, can provide a robust assessment strategy.

Designing an Effective Assessment Strategy: A well-designed assessment strategy is crucial. It should include clear goals, alignment with learning outcomes, a variety of assessment methods, diverse modes of representation, a range of tools, and personalized feedback.

Conclusions

Summary of the unit content

Benefits of Digital Assessment: Digital assessment offers numerous advantages, including increased efficiency and speed in grading, greater flexibility and accessibility for students, enhanced engagement through interactive formats, and the ability to personalize learning experiences.

Digital Assessment Defined: Digital assessment leverages technology to evaluate student performance through various methods like online quizzes, assignments, and portfolios. It offers features like automated feedback, accessibility, and data-driven insights.

UNIT 3.1

UNIT 3.2

Analysis and interpretation of learning evidences, assessment data and measurement results

In this unit we will focus how to analyze data from students and learning process.

Each of them serves a unique role in providing a comprehensive picture of student learning and program effectiveness. Institutions and instructors can use a combination of direct and indirect evidence, along with course, program, and institutional data, to ensure robust assessment practices.

In HE, the assessment process is multifaceted and focuses on various types of learning evidence and data to evaluate student achievement and progress. List of the most important learning evidence and data categories for assessment purposes:

Learning evidences and data

UNIT 3.2

Check bubbles for some of the most popular tools.

Several tools and platforms are used in HE to implement learning analytics. These tools help you to collect, analyze, and visualize student data.

Tools for learning analytics

It involves applying data-driven techniques to understand and optimize the learning process by providing insights into student performance, engagement, and behavior.

It uses various types and sources of data and assessment results, to inform instructors about students' learning progress and achievements.

Learning Analytics is the collection, analysis, and use of data related to student learning and academic activities to improve teaching and learning outcomes.

UNIT 3.2

Moodle Analytics

Microsoft Power BI

Tableau

IBM Watson Education

Canvas Analytics

Brightspace Insights

Civitas Learning Platform

UNIT 3.2

Select AI tools

Prepare and Input Data

Monitor and Adjust

Review and Reflect

Implement AI in the Assessment Process

Design assessment objectives

AI for assessment

AI is a useful tool for assessment, it automates and speeds up the assessment process and provides solutions to reduce tasks to take a significant burden off the instructor's shoulders. Take the following steps if you wish to use AI for assessment:

UNIT 3.2

objectives, select appropriate AI tools, prepare and input relevant data, and integrate AI into the assessment process. The instructor will also need to continuously monitor AI performance and make necessary adjustments to ensure effective use. Finally, the instructor will review AI-generated insights to refine future assessments and instructional strategies.

Usecase: Integrating AI into Assessment for a Higher Education Course

Scenario: A HE instructor is implementing AI to enhance the assessment process for a course. The instructor aims to use AI tools to automate grading, provide personalized feedback, and analyze student performance data to improve learning outcomes. To achieve this, the instructor must design clear assessment

Instructions: 1. Read the provided use case scenario. 2. Answer the following questions based on the steps the instructor should take to integrate AI into the assessment process.

This practical exercise helps you to understand and effectively implement AI tools in the assessment process.

Practical exercise

UNIT 3.2

AI Can Streamline and Enhance Assessment: Artificial intelligence presents opportunities to automate and accelerate assessment processes, potentially reducing instructor workload. Thoughtful implementation, involving clear objective design, careful tool selection, data preparation, ongoing monitoring, and reflective review, is crucial for effective and fair AI-assisted assessment.

A Multi-Stage Approach is Key to Leveraging AI in Assessment: Successfully integrating AI into assessment involves a structured process. This includes clearly defining assessment objectives, selecting appropriate AI tools, preparing and inputting relevant data, implementing AI in the assessment process, continuously monitoring and adjusting its performance, and finally, reviewing and reflecting on the outcomes to inform future practices.

Conclusions

Summary of the unit content

Learning Analytics Offers Data-Driven Insights: Learning analytics involves the systematic collection, analysis, and application of student learning data to enhance teaching and learning outcomes. Tools like Moodle Analytics, Canvas Analytics, Microsoft Power BI, and others help instructors track progress, identify patterns, and gain valuable insights into student performance and engagement.

Digital Assessment Defined: Digital assessment leverages technology to evaluate student performance through various methods like online quizzes, assignments, and portfolios. It offers features like automated feedback, accessibility, and data-driven insights.

UNIT 3.2

Dylan Wiliam (transcript from video)

UNIT 3.3

Effective feedback and support students to develop their learning practice

If we take the three central processes in assessments of making sure that: you are clear about where the learner is going; you are clear about where they are and you want to establish how to get there; and you think about the role of the teacher; the role of the other peers in the classroom and the learner themselves. You end up with five, what we call, key strategies. This unit focus how to use feedback for support students to develop their competences.

Feedback is specific and supportive boosting intrinsic motivation, leading to deeper engagement and better learning outcomes (Shute, 2008).

Effective feedback encourages self-regulation and persistence by making goals more attainable and meaningful (Nicol & Macfarlane-Dick, 2006).

Feedback is essential for motivation as it helps learners understand their progress, reinforces positive behaviors, and provides clear directions for improvement, fostering a sense of competence and autonomy (Hattie & Timperley, 2007).

Feedback and motivation

UNIT 3.3

tips 15-21.

tips 8-14.

tips 1-7.

21 components of effective feedback each explanation in popwindow

21 tips for giving effective feedback

UNIT 3.3

Feedback Statement

Feedback Statement

Feedback Statement

Feedback Statement

Analyze the following feedback provided and identify which of the 21 components of effective feedback are being applied.

Usecase scenario: You are an instructor providing feedback to a student who has just completed a research project. The student has performed well but there are areas for improvement, particularly in the analysis and synthesis of information.

Feedback Development Exercise

Reflective exercise

UNIT 3.3

High-Quality Feedback Fosters Self-Regulation and Deeper Learning: When feedback is specific, supportive, and timely, it empowers learners to take ownership of their learning. By making goals clearer and more achievable, it encourages self-reflection, persistence, and a deeper engagement with the learning material, ultimately leading to better learning outcomes.

A Multifaceted Approach to Feedback Delivery is Optimal: To maximize the impact of feedback, it should be delivered in appropriate amounts, be tied to action plans, and ideally come from multiple sources and in various forms (including data visualizations). Crucially, effective feedback is tailored to the individual learner, easy to understand, specific to their performance, and collaborative, encouraging the learner's input and solutions.

Conclusions

Summary of the unit content

Effective Feedback Has Specific Characteristics: Providing impactful feedback involves more than just saying "good job" or "needs improvement." It requires being specific, timely, appropriate, focused on behavior (not personality), proactive, descriptive, non-judgmental, and based on accurate information. Furthermore, it should be recurring, embedded in the learning culture, linked to learning outcomes, and guiding towards action plans.

Feedback is a Cornerstone of Motivation and Learning: Effective feedback is not just about pointing out errors; it's a powerful motivator that helps learners understand their progress, reinforces positive actions, and provides clear pathways for improvement. This, in turn, cultivates feelings of competence and autonomy, which are vital for engagement.

UNIT 3.3

Better, faster: cutting-edge online assessments in MSD Every year, the Medical Science Division's Learning Technologies (MSDLT) team supports teaching staff to run more than 160 online assessments for over 17,000 participants - everything from quizzes and open-book tests to assessments sat under exam conditions, including formal University exams. But it's not just this scale and reach that's impressive. Online assessments can also be incredibly rich, incorporating audio, video, simulations and visualisations to put students through their paces. And they can be versatile enough to process algebra and other complex mathematical elements. Link to case study:

Better, faster: cutting-edge online assessments in MSD

cASE STUDy

Dringó-Horvath, I. M. Pinter, T. (Eds.) (2021). Educational technology in higher education, methodological considerations. Károli Gáspár University.

Redecker, C., & Johannessen, Ø. (2013). Changing Assessment—Towards a New Assessment Paradigm Using ICT. European Journal of Education, 48(1), 79-96.

Jisc. (2021). Transforming assessment and feedback with technology. Jisc.org.uk

EUSurvey - Survey (europa.eu)

Shute, V. J. (2008). Focus on Formative Feedback. Review of Educational Research, 78(1), 153-189.

Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81-112.

Kearney, S., Perkins, T., & Mak, A. (2020). Digital assessment: designing for student success. Australasian Journal of Educational Technology, 36(2), 67-79.

Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu." Publications Office of the European Union.

Astin, A. W. (2012). Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. Rowman & Littlefield Publishers.

Resources

https://www.youtube.com/watch?v=_ZbgBukwD2c (2:43)

https://www.youtube.com/watch?v=y5As7zVDzRQ (4:27)

Brightspace Insights

Tableau

Civitas Learning Platform

IBM Watson Education

Microsoft Power BI

Canvas analytics

21 Components of Effective Feedback Infographic - e-Learning Infographics (elearninginfographics.com)

Delivering Great Feedback Infographic - Infographic Transcript (mindtools.com)

https://award.profformance.eu/search/readDetail/322

Wan, Ng (2015). New digital technology in education. Springer

Nicol, D. J., & Macfarlane-Dick, D. (2006). "Formative assessment and self-regulated learning: A model and seven principles of good feedback practice." Studies in Higher Education, 31(2), 199-218.

Moodle analytics

Secolsky, C., & Denison, D. B. (Eds.). (2017). Handbook on measurement, assessment, and evaluation in higher education. Routledge.

Resources

more cards

Learning evidence and data

Assessment

Digital technologies and tools for assessment

GLOSSARY

Digital technologies and tools for assessment refer to a wide-range of digital platforms, software, and applications designed to facilitate the evaluation of learning, performance, or skills in various settings. They include online quizzes, electronic surveys, e-portfolios, learning management systems (LMS), and AI-driven analytics that provide real-time feedback and enable personalized learning. They aim to streamline assessment processes, support diverse learning needs, and provide data-driven insights for improved educational outcomes.

Learning evidence refers to any documented demonstration of student knowledge, skills, or competencies that can be used to assess progress toward learning outcomes. Learning data encompasses quantitative and qualitative information collected through various tools and methods that provide insights into student learning behaviors, engagement, performance, and outcomes.

Assessment is an organic part of the entire educational process and provides information that measures its goals and content, the teaching and learning process, and the performances of the learner, while contributing to a more efficient organisation of teaching and learning from the perspectives of both instructors and students alike.

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Formative and summative assessment

Digital assessment

Effective feedback

GLOSSARY

Effective feedback is information provided to learners about their performance that is specific, timely, and constructive, aimed at guiding future improvement and learning. It should be clear and actionable, focusing on strengths and areas for development while promoting self-regulation and motivation, considering the learner's context and needs.

Digital assessment involves the use of digital technologies and tools that can enhance existing assessment practices and facilitate the collection of learning evidence and data to better support and assess learners, while enbling them to reflect and adapt the teaching and learning process and practice.

The goal of formative assessment is to monitor student learning to provide ongoing feedback that can be used by instructors to improve their teaching and by students to improve their learning, to help them identify their strengths and weaknesses and target areas that need work and to help teachers recognize where students are struggling and address problems immediately. The goal of summative assessment is to evaluate student learning at the end of an instructional unit by comparing it against some standard or benchmark. See more:

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Digital tools for assessment

Learning evidence and data

Digital technologies for assessment

GLOSSARY

Digital technologies for assessment embrace any products or services of relevant tools providing evidence and data of the students’ learning process, options for analysis and more targeted feedback and support.

Learning evidence refers to any documented demonstration of student knowledge, skills, or competencies that can be used to assess progress toward learning outcomes. Learning data encompasses quantitative and qualitative information collected through various tools and methods that provide insights into student learning behaviors, engagement, performance, and outcomes.

A wide-range of digital tools can be used for different assessment purposes, which provide data on usage of learning materials, research and consumption of information, communication and collaboration with peers, content creation and presentation, reflection and assessment of what has been learned.

Self-regulated learning (SRL)

Self-regulated learning (SRL) refers to learning that is guided by metacognition (thinking about one's thinking), strategic action (planning, monitoring, and evaluating personal progress against a standard), and motivation to learn. For more see:

Effective feedback

Personalisation of learning

Learning analytics

GLOSSARY

Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occurs. For more go to:

Personalization of learning is an educational approach that tailors instruction, content, and learning experiences to meet the individual needs, interests, and learning styles of each student. It empowers learners by allowing them to progress at their own pace, choose relevant learning paths, and engage in ways that optimize their personal success and development.

Effective feedback is specific, timely, and constructive information provided to learners that helps them understand their strengths and areas for improvement, guiding them toward achieving their learning goals.

Final Quiz of the module

10

Course completed!

Acknowledgements

Specific to the learner’s performance: Discusses how to enhance a specific part of the project.

Appropriate: Positive and constructive tone.

Focused: Highlights the importance of improving analysis.

4. Feedback Statement: "Your project shows potential, but you need to improve your analysis. This is crucial for your grade."

Components applied:

Dartmouth College

Dartmouth College outlines the "9 Principles of Good Practice for Assessing Student Learning," which serve as a foundational framework for conducting assessments in higher education. These principles include aligning assessment with educational values, employing diverse methods to capture comprehensive student learning, and involving a wide range of stakeholders in the assessment process to foster continuous improvement.

Digital assessments can be accessed remotely, allowing students to complete their assessments from various locations and at different times, depending on the assessment design.

Accessibility and flexibility

Check the case from PROFFORMANCE database Developmental evaluation in the cooperative it provides learning process provides complex assesment including students self-reflection

Example:

Interactive data visualizations, customizable dashboards, integration with multiple data sources, and drill-down analysis.

Key Features

Tableau is a powerful data visualization tool to analyze and present learning data. It integrates with various databases and LMS platforms to generate visual reports on student performance, engagement, and outcomes.

Tableau

Overview

Design assessment objectives

Clearly define the learning outcomes and objectives that the AI will assess, ensuring alignment with course goals and standards. Specify the types of assessments (e.g., quizzes, essays, projects) and criteria for evaluation.

In this sourse you will get an overview how to....SHORT description of TA

Learning Management System (LMS) Analytics:

Attendance and Participation

Data on student engagement, such as time spent on resources, interaction with course materials, and completion rates for assignments or activities.

Engagement metrics that can correlate with student success and participation in the learning process.

Course-Level Data

Assignment Grades

Scores on assignments that provide detailed insight into specific areas of strength and weakness in student learning.

Review and Reflect

After the assessment, review AI-generated insights and reports to identify trends and areas for improvement. Use the findings to inform future assessments, instructional adjustments, and personalized learning paths for students.

Monitor and Adjust

Continuously monitor the AI’s assessment processes to ensure accuracy and fairness. Make adjustments based on the system’s feedback, refining algorithms or assessment criteria as needed to improve reliability and relevance.

Implement AI in the Assessment Process

Use AI to facilitate assessments, whether by automating grading, personalizing tests, or generating real-time feedback. AI can also analyze patterns in student performance to adjust difficulty levels or suggest resources.

Select AI tools

Choose AI-powered assessment tools that match your objectives, such as automated grading systems, plagiarism detection, or adaptive testing platforms. Ensure the tools can analyze relevant data and provide insightful feedback.

Advanced data visualization, real-time dashboards, data integration from various sources, and predictive analytics capabilities.

Key Features

MS Power BI is a business analytics tool that can be customized for educational purposes. It allows you to aggregate data from multiple sources (e.g., LMS, student information systems) and create interactive reports and visualizations. Power BI enables tracking trends, analyzing student outcomes, and improving decision-making based on data.

Microsoft Power BI

Overview

Performance Tasks

These tasks assess students' ability to apply skills in real or simulated settings.

Laboratory Reports

For STEM fields, these reports assess practical application of theoretical knowledge and experimental skills.

Quizzes

Regular short assessments that provide insight into student comprehension of specific topics.

Digital Portfolios

Scholarly Papers

A collection of student work over time that showcases progress, creativity, and mastery of specific skills.

Academic papers that assess critical thinking, research abilities, and mastery of subject content.

Direct Evidence of Student Learning

Examinations

Includes midterms, finals, and other cumulative tests that assess students' understanding of course material.

Hint: You harness the power of digital technology to deliver prompt, specific, and clear feedback to students, when appropriate. Through digital platforms and tools, you ensure that feedback is tailored to individual student needs, enabling targeted guidance for their learning journey.

If you want to learn more about your strengths and the areas where you can enhance the ways in which you use digital technologies for teaching and learning, click here.

I adapt learning practices and provide support according to the results generated using digital technologies.

I use digital technology to provide timely, targeted and clear feedback to students.

I create, critically analyse, and interpret digital data on student’s activities, performance and progress.

I utilize digital technologies for diverse and appropriate assessments.

• Annotation and Feedback Tools (e.g., Turnitin Feedback Studio, Hypothesis):

Tools that allow instructors to provide detailed feedback on written work or other submissions, often integrated with plagiarism detection.

• Project and case-based assessment Tools (e.g., Google Workspace, Microsoft Teams):

Platforms that allow students to work collaboratively on complex projects or case studies, often with integrated tools for submission, feedback, and grading.

Summative assessments are typically used at the end of a learning period to evaluate overall achievement.

• Online testing platforms (e.g., ExamSoft, Survey Monkey, Respondus):

Secure online testing platforms used for high-stakes exams, etc.

Summative Assessment Tools

Ashley Finley on the Future of Assessment in Higher Education (3 minutes)

Some critical issues in enabling impactful assessment(20 minutes)

5 Formative Assessment Ideas(5 minutes)

These tutorials will help guide you think issues of creating an effective digital assessment strategy.

• Harnessing the Power of AI: Transforming Assignments and Assessments in Higher Education

This video explores how AI can transform formative assessments in higher education, emphasizing the integration of AI in designing engaging and adaptive assessments

• Artificial Intelligence Tools in Higher Education: This tutorial introduces various AI tools and explains how they can be leveraged for different assessment tasks in higher education.

Students can take assessments from anywhere, which increases accessibility for those who may not be able to physically attend campus.

Flexibility and Accessibility

These assessments can generate detailed analytics on student performance, helping educators identify trends, gaps in knowledge, and areas that may need further attention.

Data-driven insights

14. An appropriate amount: Too much feedback will overwhelm and confuse the employee. Too little feedback is not sufficient to elicit a change.

13. Tied to an action plan: Learner should know exactly how to increase performance and what steps needed to take.

12. Guiding: The information given to the learner should be used to either confirm or correct their performance.

11. Focused: Feedback should be channeled toward key result areas. Feedback should also be linked to learning outcomes.

10. Embedded in the Culture: Foster an environment of continuous feedback and professional development.

9. Recurring: Feedback should be recurring.

8. Based on accurate and credible information: The feedback should be based on accurate information. Never use rumors as examples.

Digital assessments often provide automated feedback and grading, allowing for quicker turnaround times and reducing the administrative burden on instructors.

Automated feedback and scoring

Indirect Evidence of Student Learning

Reflective Essays, Journals, Blogs

Students’ self-assessment of their learning, often reflecting on growth, challenges, and development.

Focus Groups and Interviews

Qualitative data from students about their learning experiences and perceptions of knowledge and competence development.

Surveys and Questionnaires

Self-reported data from students regarding their perceptions, understanding of learning subjects, including course evaluations, etc.

Hint: By using the digital data and results of learning analytics, you improve your teaching and you support students to use the data to the development of their learning practices.

Digital Learning Analytics

Use of Academic Support Services

Data collected from digital platforms to track and analyze student behaviors and learning patterns.

Data on how often students access tutoring, writing centers, or other support resources.

Engagement Data

Student Engagement with Course Material and Curricular Activities

Participation, involvement in, time spent with learning materials and activities, engagement and collaboration with peers.

Predictive analytics, personalized learning pathways, natural language processing, and AI-driven insights.

Key Features

IBM Watson Education provides AI-driven learning analytics solutions. It uses machine learning and natural language processing to provide insights into student performance, learning styles, and content engagement. Watson Education helps create personalized learning experiences based on data analysis.

IBM Watson Education

Overview

Both teachers and students can save time by using digital assessment. Automatic assessment speeds up scoring and feedback, allowing teachers to focus more on designing learning experiences.

Efficiency and Speed

Digital assessments reduce the need for paper, making them a more sustainable option compared to traditional exams.

Environmentally Friendly

Not given using judgmental language: No clear evidence of judgmental language.

Based on accurate and credible information: Refers to guidelines.

Specific: Identifies a problem with the analysis.

2. Feedback Statement: "Your analysis was superficial and did not meet the expected standard. Please refer to the guidelines."

Components applied:

7. Not given using judgmental language: Avoiding judgmental language will decrease the possibility that the learner will be defensive.

6. Given using descriptive language: Describing how the learner’s behavior impacts performance will help facilitate understanding.

5. Proactive: Don’t delay or avoid providing feedback. Identify issues and provide feedback before they become problems or have a large impact on the company.

4. Focus on behavior, not personality: Always provide feedback that is based on behavior, not personality or characteristics unless absolutely necessary.

3. Appropriate: Feedback should be presented in a positive, tactful and non-threatening manner.

2. Timely: Learner must receive the feedback as close to the assessment as possible.

1. Specific: Feedback must be concrete and relate to a specific, measureable performance goal.

Hint: You gather valuable data on student engagement, learning outcomes, and areas of improvement by utilizing various digital tools and learning management system. Through careful analysis and interpretation of this data (learning analytics), you gain insights into individual student needs and adapt your teaching strategies accordingly. You explore how digital data can inform instructional decisions and promote personalized learning experiences tailored to student's unique requirements.

Interactive dashboards, grade distribution analysis, and customizable reports. Canvas also supports integrations with third-party analytics tools for enhanced data analysis.

Key Features

Canvas LMS offers built-in analytics that allow instructors to monitor student performance and engagement in real-time. It includes data visualizations of student activity, grades, and assignment submissions, enabling educators to intervene early if students are falling behind.

Canvas Analytics

Overview

• ePortfolios (e.g., Portfolium, Mahara, FolioSpaces):

Digital portfolios where students can compile and reflect on their work over a course or program, providing a comprehensive view of their learning progress. ePortolios can be also use for summative assessment.

• Reflective Journals and Blogs (e.g., WordPress, Blogger):

Online platforms where students can regularly reflect on their learning experiences, which can be assessed for depth of reflection and personal growth.

• LMS Discussion Tools:

Tools for facilitating and assessing online discussions, often used for asynchronous collaborative learning.

• Peer and self-assessment tools (e.g., Peergrade, Peermark, Google Docs):

These tools allow students to evaluate their own or their peers’ work, which promotes reflection and critical thinking.

• Learning Management Systems (LMS) integrated quizzes (e.g., Canvas, Blackboard, Moodle):

LMS platforms often have built-in quiz tools that can be used for both formative and summative assessments.

• Polls and surveys (e.g., Mentimeter, Poll Everywhere):

Useful for gathering instant feedback on student understanding and opinions during lectures.

Formative assessments are used to provide ongoing feedback during the learning process.

• Quizzes (e.g., Quizlet, Socrative, Edmodo, Google Forms, Kahoot!):

These tools allow for quick, low-stakes assessments that help gauge student understanding in real-time.

Formative Assessment Tools

Professional coordinators, advisers Vilmos Vass Szilvia Besze Adviser Daliborka Luketic Designer Szabina Gyurisán Horváthné Co-designers Linda Huszár Bianka Bozzay

Authors Jan Beseda Loboda Zoltán Horváth-Dringó Ida Reviewers Horváth-Dringó Ida Anca Greere

Acknowledgements

Digital assessments can be adaptive, adjusting the difficulty of questions based on student responses. This can create a more personalized learning experience tailored to individual needs.

Personalized Learning

Hint: You embrace a wide range of assessment formats and methodologies to promote diversity and ensure appropriateness for different learners and contexts. Through digital tools, apps and platforms, you create interactive assessments that allow students to demonstrate their understanding in various ways, such as multimedia projects, online quizzes, collaborative discussions, and virtual simulations. You apply flexible assessment strategy that values individual strengths, fosters creativity, and encourages active participation through the integration of digital technologies.

Focused: Centers on a key aspect of the project.

Guiding: Suggests improvement in a specific area.

Specific: Addresses a particular part of the project.

1. Feedback Statement: "You did a good job on the project overall, but the analysis section could use more depth."

Components applied:

• Discussion Boards and Forums (e.g., LMS Discussion Tools):

Tools for facilitating and assessing online discussions, often used for asynchronous collaborative learning.

• Peer Review Systems (e.g., PeerMark, Peergrade):

Systems that allow students to give and receive feedback on assignments, supporting the development of evaluative and critical thinking skills.

These tools emphasize teamwork and collective problem-solving as part of the learning process.

• Collaborative Learning Platforms (e.g., Padlet, Miro, Trello):

Tools that allow students to work together on group projects or shared boards, often with real-time collaboration features.

These tools assess students’ ability to apply knowledge in real-world or simulated scenarios.

• Digital content creation and presentation tools (e.g., blogs, Padlet, presentation tools, word processing, worksheets, etc.):

Tools for creating and delivering digital content and presentations, allowing assessment of communication and presentation skills.

Performance-Based Assessment Tools

Digital assessment involves the use of digital tools, platforms, and software to evaluate students' academic performance. This can include online quizzes, assignments, exams, simulations, and digital portfolios

Technology-enabled assessment

Vanderbilt University

Vanderbilt University offers a comprehensive guide on various assessment methods: self-assessment, peer assessment, essays, and exams. The guide emphasizes the importance of diverse assessment approaches to enhance reliability and improve overall student performance. It also discusses strategies for integrating these methods effectively into the teaching and learning process.

Digital assessments featuring multimodality of representation can increase student engagement, making the assessment process interactive and enjoyable.

Engagement

Digital assessments can take various forms, offering various methods to assess student learning and understanding.

Versatility

• Pre-Tests and Diagnostic Quizzes (e.g., LMS Quiz Tools, Socrative):

Short quizzes that assess baseline knowledge and help instructors tailor instruction based on student needs.

Diagnostic assessments help identify students' prior knowledge, strengths, and areas for improvement before instruction begins.

Diagnostic Assessment Tools

21. Collaborative: Allowing learner to contribute to the feedback process and offer solutions will help them accept the feedback more readily.

20. Specific to the learner’s performance: Managers should not include factors that are beyond the control of learner in the feedback process.

19. Easy to understand: Feedback should be easy to understand and the employee should repeat back the information discussed.

18. Tailored to the recipient: The individual learner’s characteristics, level of performance and cognitive processing style should influence the type of feedback they receive.

17. From data: Quantitative measures of performance data should be presented in a meaningful way and should be used as concrete examples.

16. In many forms: Graphs and charts that track individual and group performance are imperative to the feedback process.

15. From multiple sources: In order to internalize the feedback and elicit change, learner should receive feedback from multiple sources (peers, etc.).

Predictive analytics, custom reports, and visual dashboards for tracking student behavior and progress within Moodle courses.

Key Features

Moodle is a Learning Management System (LMS), that has integrated learning analytics capabilities. It provides data on student engagement, progress, and performance within courses. Instructors can track activities like course participation, quiz results, and resource usage to identify at-risk students and tailor interventions.

Moodle Analytics

Overview

Predictive analytics, performance dashboards, customizable reports, and early alert systems.

Key Features

Brightspace provides analytics on student engagement, performance, and content usage. Instructors can use the data to personalize learning and offer targeted interventions to students.

Brightspace Insights

Overview

Prepare and Input Data

Provide the AI system with the necessary data, such as previous assessment results, rubrics, and grading criteria. Ensure the data is clean, well-organized, and sufficient for the AI to process effectively.

Columbia University

Columbia University provides an assessment guide that focuses on evaluating student learning in alignment with institutional goals and mission statements. The guide emphasizes the importance of ongoing assessment practices and utilizing data to improve both student outcomes and educational programs.

Self-Assessment

Peer Assessment and Review

Opportunities for students to reflect on their own learning, providing insight into their self-perception of skills and knowledge.

Assessment by fellow students, which provides a different perspective on the quality and effectiveness of learning.

Peer and Self-Assessment

Guiding: Offers concrete steps to improve.

Tied to an action plan: Suggests reviewing the rubric for criteria.

Timely: Implies a focus on the current project.

Specific: Provides clear directions for improvement.

3. Feedback Statement: "To improve your analysis, focus on incorporating more detailed evidence and connecting it to your conclusions. Review the provided rubric for specific criteria."

Components applied:

• Learning Analytics Platforms (e.g., Civitas Learning, Brightspace Insights):

Advanced platforms that aggregate data from multiple sources to provide insights into student progress and inform institutional decision-making.

• LMS Analytics (e.g., Canvas Analytics, Blackboard Analytics):

Built-in tools that track student engagement, assignment submission patterns, and performance across courses.

These tools use data to assess student engagement, progress, and outcomes.

Learning Analytics and Data Tools

Digital tools provide detailed insights into student performance, including metrics such as time spent on each question, patterns of incorrect answers, and overall trends. This data can help educators improve course design and delivery.

Data Analytics

Predictive analytics, student success dashboards, intervention tracking, and real-time alerts for educators.

Key Features

Civitas Learning offers a comprehensive learning analytics tool focused on student success. It aggregates data from various institutional systems to provide insights into student behaviors, academic performance, and potential risks. The platform uses predictive analytics to identify at-risk students and recommend interventions.

Civitas Learning Platform

Overview

If you want to learn more about your strengths and the areas where you can enhance the ways in which you use digital technologies for teaching and learning, click here. ​ This self-assessment tool develped by the EU Joint Research Centre helps you reflect on your digital competence as a higher education teacher. The tool consists of 25 questions of self-reflection to give you feedback with suggestions and tips on how to improve your teaching.