Education is a powerful catalyst for personal and professional growth, enabling individuals to acquire knowledge, skills, and perspectives that shape their lives and careers.
But how can we truly gauge the effectiveness and transformative power of learning experiences? This is where measuring the impact of learning comes into play.
Measuring the impact of learning is a multidimensional process that seeks to evaluate the outcomes, changes, and benefits derived from educational programs, training initiatives, and individual learning endeavours.
It enables educators, organizations, and learners themselves to assess the tangible and intangible effects of education, answering crucial questions such as: Did the learning experience lead to behavioural changes? Has it improved performance or contributed to organizational success? Are individuals equipped to apply their newfound knowledge and skills effectively?
Traditionally, the focus of educational evaluation has been on assessing knowledge acquisition and retention through tests and assessments. However, true impact extends far beyond mere memorisation of facts and figures. It encompasses the translation of knowledge into action, the development of critical thinking abilities, and the transformation of individuals’ beliefs, attitudes, and behaviours.
In this article, we begin by exploring the complex topic of measuring the impact of learning, exploring diverse methodologies and approaches in general and then, specifically some of the tools and features available in CLANED® that help to illuminate the true extent of education’s influence, through data-driven insights.
Methods of Measuring Learning Impact
By comprehensively understanding the principles and strategies behind measuring the impact of learning, educators and organizations can make informed decisions, optimise their educational interventions, and drive meaningful change.
Furthermore, learners themselves can gain insights into their growth, identify areas for improvement, and take ownership of their ongoing development.
To begin, let us explore some of the various methods used and the situations in which they are often deployed. This goal is not only to expose some of the methods of measuring impact available but also to frame them within a context.
Much like measuring learner success, the context, program goals, and context is playing a critical role in determining the best method used and understanding why and what the results measure.
To measure the impact of a learning program on behaviour change or business outcomes, here are some common approaches:
Surveys and interviews
Conduct post-training surveys or interviews to gather self-reported data on changes in behaviour, attitudes, and knowledge. Ask participants about specific actions they have taken or plan to take as a result of the training.
This can provide valuable insights into the immediate impact on behaviour. Using a survey tool such as Google Forms or Typeform are a good approach to this, not only do the free up time but inputs can be collected as a data source for further analysis.
Observations and assessments
Observe participants in real-life settings or simulated scenarios to assess their application of newly acquired skills. This can involve evaluating their performance, interactions, and decision-making. Assessments can be conducted through role-plays, case studies, or practical assignments to measure the application of knowledge and skills.
Often these can be done as part of the training, or as a follow-up. Regardless of when they are used, they can be considered a critical element of any effective training or training assessment since they provide a low-pressure way for learners to “practice” applying the skills and knowledge. The results of this method can be used to inform further training in low-performance areas – further focusing the training on the needs of learners.
Performance data analysis
Analyze performance data or key performance indicators (KPIs) related to the desired outcomes. For example, in a course about the effects of climate change and what individuals or organizations can do to minimise their impact, tracking metrics like energy consumption, waste reduction, or carbon footprint to gauge the impact on participants’/organizations behaviour.
In the sales training example, analyze sales figures, customer satisfaction ratings, or market expansion to assess the impact on business outcomes.
Control groups or A/B testing
Compare the performance of participants who received the training with a control group that did not. By establishing a control group, you can isolate the impact of the training program and assess any differences in behaviour or outcomes between the two groups. A/B testing can also be used to test different versions of a training program and measure their impact.
Often in learning and training scenarios this can be done using a pilot-group. While the aim of this approach generally is slightly different – used to assess the effectiveness, of the program and identify areas that can be improved prior to a wider release, the outcomes are inherently tied to its impact.
Feedback from stakeholders
Seek feedback from stakeholders who interact with the trained individuals, such as customers, colleagues, or supervisors. This can provide insights into any noticeable changes in behaviour or performance and help assess the impact of the learning program from an external perspective.
These can be especially useful areas such as customer service and support and easily correlated with “hard-data” such as successful, timely resolution of support requests, reviews and other customer metrics.
Long-term follow-up
Consider conducting follow-up assessments or surveys at regular intervals after the training program to gauge the sustained impact on behaviour and outcomes. This can help determine if the changes observed immediately after the training are maintained over time.
These are especially important when seeking long-term changes and dealing with complex or challenging topics. As the difficulty increases so does the timeline to see the full affects in action. Consider regular supporting activities and discussions in such cases, create summary reports following the support activities or discussions to help track improvements over time, and remember that improvement is rarely a linear progression.
Case studies and success stories
Collect and document case studies or success stories of individuals who have successfully implemented the learned knowledge or skills. These anecdotal accounts can provide qualitative evidence of the impact on behaviour and outcomes.
Further the act of delving in deeply to the issue, solution and outcomes often brings additional insights which otherwise may go unnoticed.
Data analysis and reporting
Analyze the collected data and present the findings in a comprehensive report. Quantitative data such as survey results, performance metrics, and business outcomes can be presented alongside qualitative data, including quotes from participants or stakeholders. This holistic view will provide a clear picture of the impact of the learning program.
With the right tools and correct insights, these reports can include outputs from all the above discussed methods, the real trick here is discovering what questions to ask and how to use the available data to help expose the affects and impact you and your course or program is seeking to deliver.
Pros, Cons and Example of These Methods
So, what is the best method for you to use? Well hopefully by now it is clear that this depend on you objectives, course/program, learners, and the tools you have access to, but in general, by combining multiple measurement approaches and data sources, you can gain a more comprehensive understanding of the impact of a learning program on behaviour change and desired outcomes, put another way “learning that works”.
To further explore what works in what situations let us examine the pros and cons of the above mentioned methods and some examples of scenarios where they can be effectively applied.
Surveys and interviews:
Pros:
- Provides direct feedback from learners, allowing them to reflect on their experience and self-report changes.
- Offers insights into attitudes, beliefs, and intentions, as well as perceived knowledge and skills.
- Allows for large-scale data collection and analysis.
Cons:
- Relies on self-reported data, which may be subject to biases or inaccuracies.
- Limited to capturing participants’ perspectives and may not capture objective changes in behaviour or outcomes.
Examples:
- Use surveys to assess participants’ awareness, attitudes, and intentions regarding climate change and sustainable practices after a course on environmental education.
- Conduct interviews to gather feedback from sales representatives on how the sales training program influenced their communication skills and approach to customer interactions.
Observations and assessments:
Pros:
- Provides direct evidence of behaviour change and application of learned skills.
- Enables the evaluation of performance in real-life or simulated settings.
- Allows for objective measurement and assessment of competency levels.
Cons:
- Requires resources and time for observation and evaluation.
- May not capture the full range of behaviours or skills in limited observation periods.
- Contextual factors in simulations may differ from real-life situations.
Examples:
- Observe participants engaging in role-plays to assess their negotiation skills and ability to apply conflict resolution techniques learned in a leadership training program.
- Evaluate the performance of medical students during clinical rotations to measure their application of medical knowledge and patient interaction skills.
READ: Assessments in Complex Product Trainings
Performance data analysis:
Pros:
- Provides objective data on business outcomes or performance indicators.
- Allows for quantitative measurement of the impact on productivity, sales, customer satisfaction, or other relevant metrics.
- Enables comparisons between pre- and post-training data to evaluate improvements.
Cons:
- Requires access to reliable performance data and metrics.
- External factors may influence outcomes, making it challenging to attribute changes solely to the learning program.
Examples:
- Analyze sales figures and customer feedback to determine the impact of a product-specific sales training program on revenue generation and customer satisfaction.
- Track employee productivity metrics and error rates to measure the impact of a software training program on efficiency and quality.
Control groups or A/B testing:
Pros:
- Facilitates comparison between a group that receives the training and a control group that does not, isolating the impact of the learning program.
- Helps establish causality by identifying changes specifically attributable to the training.
- Allows for experimental design and statistical analysis.
Cons:
- Requires careful design and implementation to ensure proper randomization and control.
- May not be feasible or ethical in all contexts.
Examples:
- Randomly assign a portion of participants to receive a financial literacy course, while the control group receives no training. Compare the financial behaviours and decision-making of both groups to measure the impact of the course on financial management skills.
- Split a group of customer service representatives into two groups, with one receiving training on empathy and communication skills, while the other does not. Assess customer satisfaction ratings to determine the impact of the training on service quality.
Feedback from stakeholders:
Pros:
- Provides external perspectives on behaviour change or performance improvement.
- Offers insights into the impact of the learning program on individuals’ interactions with others.
- Helps identify changes that may not be self-reported by learners themselves.
Cons:
- Stakeholder perspectives may be subjective and influenced by personal biases or limited observations.
- Gathering feedback from stakeholders may be challenging or time-consuming.
Examples:
- Collect feedback from supervisors or colleagues to evaluate changes in teamwork and collaboration skills resulting from a team-building training program.
- Seek input from customers or clients to assess the impact of a customer service training program on their experience and satisfaction levels.
Long-term follow-up:
Pros:
- Assesses the durability and sustainability of behaviour change or performance improvement.
- Helps determine whether changes observed immediately after the training are maintained over time.
- Provides insights into the long-term impact and effectiveness of the learning program.
Cons:
- Requires resources and commitment for extended follow-up periods.
- Participants may experience additional influences or interventions that affect the observed changes.
Examples:
- Conduct a survey six months after a leadership development program to assess whether participants continue to apply leadership strategies and behaviours learned during the program.
- Track employee performance metrics over a year after a diversity and inclusion training to evaluate the long-term impact on creating an inclusive work environment.
By using a combination of these measurement approaches, educators and organizations can obtain a comprehensive understanding of the impact of learning programs.
Each approach has its own strengths and limitations, and the choice of methodology depends on the specific context, desired outcomes, available resources, and the nature of behaviour change or business objectives being evaluated.
Measuring Learning Impact on Claned Platform
Now that we understand some of the fundamentals of measuring impact and how we can go about it, let’s explore how the CLANED® platform specifically enables measuring impact through inbuilt analytics and comprehensive data exports.
Content engagement analysis
By tracking learner behaviours such as content views, time spent on different materials, and frequency of visits, CLANED can identify trends in content engagement. This data can indicate which topics or materials are most interesting or frequently accessed by learners, highlighting the areas that have made a significant impact.
It may also shed light on topics where knowledge gaps persist and updating, adding content or further training is required. This can be a powerful insight for continuous development within the workplace and organizations to deliver the types of training employees need, supporting them and their development effectively.
Example: CLANED can identify that learners are repeatedly accessing materials related to sustainable energy solutions in a climate change course, suggesting a high level of interest and potential impact on their awareness and knowledge in that area.
Challenging or confusing topics identification
Through data analysis, CLANED can identify topics or materials where learners are spending more time, revisiting frequently, or expressing confusion or difficulty. This helps pinpoint areas where learners may be facing challenges and where further support, or instructional improvement may be needed.
Example: Claned can detect that learners are spending a considerable amount of time on specific calculus concepts in a mathematics course, indicating a challenging area that requires additional clarification or instructional support. This may sound similar to the the above example, and in some ways it is.
To differentiate interesting versus challenging content, course admins would want to include additional metrics to help clarify the picture, this could include looking at test or quiz results related to the topic, or analysing the comments and questions learners make on the related content to help determine if the frequency of engagement is the result of challenge or interest.
To help with this CLANED offers an additional tool: Content Ratings – this low threshold interaction can be displayed at the end of content and asks learners to rank the content across 3, 5 point scales – “interest”, “difficulty” and “perceived skill level”.
This simple report is surprisingly powerful, and CLANED’s backend machine learning analyses the ratings and groups learners according to similar reporting. This data is then available in a board analytic which displays the different groups of challenge, the learner in each group and the connecting factors defining the group.
By leveraging these capabilities, applying some of the methods mentions and utilising CLANED’s analytic and data features you can gain valuable insights into learner behaviours, engagement, and understanding. These insights can be used to identify and measure areas of impact, personalize learning experiences, and inform learning design and course improvements, leading to better learning experiences and outcomes.
Value and Importance of Using Data and Data Analytics in Measuring the Impact of Learning
In the realm of measuring the impact of learning, the utilisation of data and data analytics has emerged as a transformative approach that offers significant value and importance.
While traditional methods like feedback surveys and subjective measurements have their merits, leveraging data and analytics provides several advantages, ultimately leading to a more holistic, comprehensive, and insightful assessment of the impact of learning.
One of the key advantages of using data and analytics is the ability to capture objective and quantitative measurements. Data-driven approaches allow for the collection and analysis of large-scale, structured datasets, providing robust evidence of the impact of learning interventions.
By tracking learner behaviours, progress, and performance data, organizations and educators gain access to objective indicators such as test scores, completion rates, or performance metrics, offering tangible evidence of the outcomes and effects of the learning programs.
READ: What is Learning Analytics?
Moreover, data analytics enables the identification of patterns, trends, and correlations that may be otherwise overlooked. By employing sophisticated algorithms and techniques, it becomes possible to uncover hidden insights within the data.
For example, analysing learner engagement with specific content or identifying common challenges in comprehension can unveil critical areas for improvement and help refine instructional strategies. These data-driven insights provide a more precise understanding of the impact and effectiveness of learning interventions, enabling educators and organizations to make data-informed decisions.
By combining data and analytics with subjective measurements, such as feedback surveys or interviews, a more comprehensive and well-rounded perspective is achieved. While subjective measurements offer valuable insights into learners’ perceptions and experiences, they often suffer from biases and limitations. Data-driven approaches provide an objective lens that complements the subjective viewpoints, providing a more robust and reliable assessment of the impact of learning. The integration of both quantitative and qualitative data allows for a deeper understanding of learner behaviour, performance, and the broader context in which learning occurs.
Furthermore, the use of data and analytics allows for ongoing monitoring and evaluation of the impact of learning over time. By continuously collecting and analysing data, organizations and educators can track progress, identify areas of improvement, and adapt interventions accordingly. This iterative process of data-driven evaluation ensures that learning programs are continuously optimised, leading to more effective and impactful outcomes.
Conclusion
In summary, learning data and data analytics offer tremendous value and importance in measuring the impact of learning.
By leveraging objective measurements, uncovering hidden insights, and complementing subjective viewpoints, data-driven approaches provide a comprehensive, evidence-based assessment.
The integration of data and analytics with traditional methods results in a more holistic understanding of the impact of learning, enabling educators and organizations to make informed decisions, improve instructional strategies, and drive meaningful change in the learning landscape.
