登录查看更多内容

How can data be used to evaluate the effectiveness of blended learning?

由人工智能和领英社区提供技术支持

Blended learning, which combines online and face-to-face instruction, has become a popular option for many higher education institutions. But how can educators and administrators measure the effectiveness of this approach? Data can provide valuable insights into the learning outcomes, engagement, and satisfaction of students and instructors. In this article, we will explore some of the ways that data can be used to evaluate the effectiveness of blended learning.

此文章中的业界达人

由社区从 5 条内容中精选。了解更多

Prof.Dr.Koteswara Rao, Kolli

Senior Professor, Quality Advisor & Director - KL University, Hyd; Member - University Research Board; Advisor- ODL &…
Dr. Ranjidha Rajan

Assistant Professor of Computer Science at MSU Denver
Dr. Atul Kumar

Dean & Professor at Dr. D. Y. Patil B-School, Pune, India

1 Data sources

One of the first steps to evaluate blended learning is to identify the relevant data sources. These can include learning management systems (LMS), student information systems (SIS), surveys, assessments, feedback, and analytics tools. Each data source can offer different types of information, such as student demographics, enrollment, attendance, participation, performance, progress, retention, and satisfaction. By collecting and integrating data from multiple sources, educators and administrators can get a more comprehensive and accurate picture of the blended learning experience.

添加您的观点

Prof.Dr.Koteswara Rao, Kolli

Senior Professor, Quality Advisor & Director - KL University, Hyd; Member - University Research Board; Advisor- ODL & OL; Member- CIQA; Member-Academic Council: Member - IQAC Advisory Committee and Chairman, RPAC - KLHBS
举报内容
A variety of data such as learning management system, sis - surveys, assessments, feedback and analytics student information system which reveals -enrollment, participation, performance etc.considered as quantitative data. This includes exam scores, grades and success rates for both online and traditional components of blended courses. Analyzing this data helps to gauge whether the blend of in person and online elements positively influencees student achievement. Understanding the student satisfaction, engagement levels and perceived effectiveness of this model contributes to valuable qualitative data. Combining quantitative metrics with qualitative insights provides a comprehensive understanding of the impact of blended learning.

已翻译

赞

2 Data analysis

The next step is to analyze the data and extract meaningful insights. Data analysis can help answer questions such as: How are students and instructors engaging with the online and face-to-face components of blended learning? What are the strengths and weaknesses of the blended learning design and delivery? How are students achieving the intended learning outcomes and competencies? How are students and instructors satisfied with the blended learning experience? Data analysis can involve various methods, such as descriptive statistics, inferential statistics, correlation, regression, clustering, and visualization.

添加您的观点

Dr. Ranjidha Rajan

Assistant Professor of Computer Science at MSU Denver
举报内容
Data can be used to evaluate the effectiveness of blended learning by analyzing student performance metrics, such as grades, test scores, and completion rates, to identify trends and areas for improvement. Engagement data, including logins, participation in discussions, and assignment submissions, provide insights into how actively students interact with online and offline components. Additionally, feedback surveys and questionnaires can offer qualitative data on student satisfaction and perceived learning outcomes, enabling educators to adjust and tailor the blended learning experience to better meet learners' needs.

已翻译

赞
Mateus Amaral

Customer Care {Experience | Support | Onboarding | Success}
举报内容
Information is key. Information is power. Analyzing direct data — such as attendance, completion and overall participation; grading, even — is key to interpreting what is happening with the students and any inconsistencies and threats to the learning process. When it comes to blended learning — since it requires active discipline and participation from the students themselves, it is essencial to identify the flaws throughout the path. From analyzing facts and students' own perceptions (not everything is numbers), you can adapt quickly to what needs changing to enhance the learning experience and make it meaningful.

已翻译

赞

3 Data interpretation

The third step is to interpret the data and make sense of the findings. Data interpretation can help explain the reasons behind the results and identify the factors that influence the effectiveness of blended learning. Data interpretation can also involve comparing the data with benchmarks, standards, expectations, and best practices. For example, educators and administrators can compare the data with the Quality Matters standards for online and blended learning, or with the results of similar courses or programs.

添加您的观点

Dr. Atul Kumar

Dean & Professor at Dr. D. Y. Patil B-School, Pune, India
举报内容
In the process of data interpretation, uncover the underlying insights and implications to understand the effectiveness of blended learning. By examining factors influencing outcomes and comparing data with relevant benchmarks and standards, educators can gain valuable insights into their program's performance. Utilizing frameworks like Quality Matters and analyzing results against similar initiatives enhances understanding and informs strategic decision-making for continuous improvement.

已翻译

赞

4 Data communication

The fourth step is to communicate the data and share the insights with the relevant stakeholders. Data communication can help inform, persuade, and inspire action. Data communication can involve various formats, such as reports, dashboards, presentations, infographics, and stories. Data communication should be clear, concise, and compelling, and highlight the key messages and recommendations. For example, educators and administrators can use data to showcase the achievements and challenges of blended learning, and to propose improvements and innovations.

添加您的观点

5 Data utilization

The final step is to utilize the data and act on the insights. Data utilization can help improve the quality and effectiveness of blended learning. Data utilization can involve various actions, such as revising the blended learning design and delivery, enhancing the online and face-to-face interactions, providing feedback and support to students and instructors, adjusting the assessment and evaluation methods, and implementing new technologies and strategies. Data utilization should be based on evidence, goals, and priorities, and should be monitored and evaluated for impact.

Blended learning can offer many benefits for higher education, such as flexibility, accessibility, personalization, and collaboration. However, to ensure the success of this approach, it is essential to use data to evaluate its effectiveness. By following the steps of data sources, analysis, interpretation, communication, and utilization, educators and administrators can leverage data to enhance the blended learning experience for students and instructors.

添加您的观点

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Abdul Razak Najem

consultant freelance in both higher education development and development studies
举报内容
Learner "Self-assessment and evaluation, coupled with mentored tools, facilitate crosschecking and categorizing the significance of each datum in the educational process. This approach offers an overview of the effectiveness and interactions among various tools and data."

已翻译

赞

Higher Education

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How can data be used to evaluate the effectiveness of blended learning?

1

2

3

4

5

6

1 Data sources

2 Data analysis

3 Data interpretation

4 Data communication

5 Data utilization

6 Here’s what else to consider

Higher Education

给文章评分

感谢您的反馈

更多Higher Education相关文章

更多相关阅读内容

How can data be used to evaluate the effectiveness of blended learning?

1

2

3

4

5

6

1 Data sources

2 Data analysis

3 Data interpretation

4 Data communication

5 Data utilization

6 Here’s what else to consider

Higher Education

给文章评分

感谢您的反馈

查看其他技能