LabBuddy – Backed by the science of education
The e-learning application provided by LabBuddy has been designed and is continuously improved based on scientific literature (Van der Kolk, 2013; Verstege, 2021). On this page, we provide some additional background information related to our e-learning application.
All below mentioned educational theories are used to develop LabBuddy’s e-learning application. Our scientists and developers continue to improve the application using the newest insights in educational sciences and follow the latest EdTech developments.
The ‘four-component instructional design’ (4C/ID) model (Van Merriënboer, 2018) addresses complex learning. The four components are as follows:
Any learning task should be designed based on a whole authentic real-life task. The difference in learning tasks provided for first-year versus last-year courses lies in their complexity and the support students receive. Another important aspect of the theory is the distinction between supportive information (the theory behind whole-task) and procedural information ('how to' information relevant for recurrent aspects of tasks). To achieve all the intended learning outcomes of the laboratory classes, students should enter the laboratory well-prepared. This can best be achieved by studying supportive information since processing supportive information often requires a lot of processing in the working memory (hence increasing the risk of cognitive overload, should this be done during the laboratory class). On the other hand, processing procedural information requires less cognitive processing. Therefore, this kind of information can best be presented ‘just in time’.
In line with the 4C/ID model, LabBuddy’s e-learning application supports students in preparing for laboratory work beforehand so that they have already processed a lot of supportive information before entering the laboratory. The same application will provide students with just in time information during laboratory work, hence taking away many potential low-level questions that students could ask (e.g., where can I find …). LabBuddy supports teachers to create meaningful learning experiences during laboratory education.
Learning materials that use multimedia, on the condition that the graphics are not solely intended for decoration, keep students’ attention for a longer time (Keller, 1987), and were found to promote active and deep learning (Clark & Mayer, 2016). Based on solid evidence from scientific literature, Mayer’s cognitive theory of multimedia learning provides principles for designing multimedia learning materials (Mayer, 2009). Such principles, for example, focus on directing the learner’s attention and minimizing unnecessary cognitive load.
Multimedia learning plays a significant role in LabBuddy’s e-learning application, that is why many of Mayer’s principles were incorporated during its design.
Cognitive overload is one of the major problems during laboratory classes. Students are continuously bombarded with new information, leaving only a minimal time to process all the latest information. The cognitive load theory implies that instructional design should take the cognitive architecture of humans into account. This architecture consists of both a working memory with a limited capacity and a long-term memory with a nearly unlimited capacity. When a task requires the learner to hold too many pieces of information in the working memory simultaneously, the learner's working memory will be 'overloaded', which results in less learning (Sweller, 1994).
LabBuddy’s e-learning application was developed in such a way that it decreases the cognitive load imposed on students during laboratory work. The design choices are, among others, based on the following educational theories.
Based on the constructive alignment theory, there should be a clear relationship between the intended learning outcomes, the learning activities, and the assessment (Biggs & Tang, 2011). However, for traditional ('cookbook') laboratory classes this is often not the case, which, for example, results in students not preparing before they enter the laboratory classes. When this is the case, it is usually worth applying the constructive alignment theory to the laboratory course.
LabBuddy’s e-learning application provides teachers with novel learning activities (including preparation activities), making it easier for teachers to realize constructive alignment in their laboratory courses.
When designing learning material, the designer should make sure to create a positive learning experience. One way to do so is by introducing motivational elements. Motivational elements can improve student learning by fostering cognitive processing aimed at making sense of the material (Mayer, 2014). The ARCS model of motivation identifies four motivational components (attention, relevance, confidence, and satisfaction) that should all be considered to get and keep students motivated (Keller, 1983).
LabBuddy’s e-learning application incorporates all four motivational components, for example, by providing challenging exercises, hints, and answer-specific feedback. Based on the content of any specific laboratory course and teachers' wishes, many additional motivational components can easily be introduced in LabBuddy’s e-learning application.
The ‘four-component instructional design’ (4C/ID) model (Van Merriënboer, 2018) addresses complex learning. The four components are as follows:
Any learning task should be designed based on a whole authentic real-life task. The difference in learning tasks provided for first-year versus last-year courses lies in their complexity and the support students receive. Another important aspect of the theory is the distinction between supportive information (the theory behind whole-task) and procedural information ('how to' information relevant for recurrent aspects of tasks). To achieve all the intended learning outcomes of the laboratory classes, students should enter the laboratory well-prepared. This can best be achieved by studying supportive information since processing supportive information often requires a lot of processing in the working memory (hence increasing the risk of cognitive overload, should this be done during the laboratory class). On the other hand, processing procedural information requires less cognitive processing. Therefore, this kind of information can best be presented ‘just in time’.
In line with the 4C/ID model, LabBuddy’s e-learning application supports students in preparing for laboratory work beforehand so that they have already processed a lot of supportive information before entering the laboratory. The same application will provide students with just in time information during laboratory work, hence taking away many potential low-level questions that students could ask (e.g., where can I find …). LabBuddy supports teachers to create meaningful learning experiences during laboratory education.
Learning materials that use multimedia, on the condition that the graphics are not solely intended for decoration, keep students’ attention for a longer time (Keller, 1987), and were found to promote active and deep learning (Clark & Mayer, 2016). Based on solid evidence from scientific literature, Mayer’s cognitive theory of multimedia learning provides principles for designing multimedia learning materials (Mayer, 2009). Such principles, for example, focus on directing the learner’s attention and minimizing unnecessary cognitive load.
Multimedia learning plays a significant role in LabBuddy’s e-learning application, that is why many of Mayer’s principles were incorporated during its design.
Cognitive overload is one of the major problems during laboratory classes. Students are continuously bombarded with new information, leaving only a minimal time to process all the latest information. The cognitive load theory implies that instructional design should take the cognitive architecture of humans into account. This architecture consists of both a working memory with a limited capacity and a long-term memory with a nearly unlimited capacity. When a task requires the learner to hold too many pieces of information in the working memory simultaneously, the learner's working memory will be 'overloaded', which results in less learning (Sweller, 1994).
LabBuddy’s e-learning application was developed in such a way that it decreases the cognitive load imposed on students during laboratory work. The design choices are, among others, based on the following educational theories.
Based on the constructive alignment theory, there should be a clear relationship between the intended learning outcomes, the learning activities, and the assessment (Biggs & Tang, 2011). However, for traditional ('cookbook') laboratory classes this is often not the case, which, for example, results in students not preparing before they enter the laboratory classes. When this is the case, it is usually worth applying the constructive alignment theory to the laboratory course.
LabBuddy’s e-learning application provides teachers with novel learning activities (including preparation activities), making it easier for teachers to realize constructive alignment in their laboratory courses.
When designing learning material, the designer should make sure to create a positive learning experience. One way to do so is by introducing motivational elements. Motivational elements can improve student learning by fostering cognitive processing aimed at making sense of the material (Mayer, 2014). The ARCS model of motivation identifies four motivational components (attention, relevance, confidence, and satisfaction) that should all be considered to get and keep students motivated (Keller, 1983).
LabBuddy’s e-learning application incorporates all four motivational components, for example, by providing challenging exercises, hints, and answer-specific feedback. Based on the content of any specific laboratory course and teachers' wishes, many additional motivational components can easily be introduced in LabBuddy’s e-learning application.
References
Biggs, J. and Tang, C. (2011). Teaching for Quality Learning at University. New York, NY: McGraw-Hill Education.
Clark, R.C. and Mayer, R.E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. Hoboken: NJ: John Wiley & Sons.
Keller, J.M. (1987). “Development and use of the ARCS model of instructional design”. Journal of Instructional Development 10, 2-10. DOI: https://doi.org/10.1007/bf02905780.
Mayer, R.E. (2009). Multimedia learning. New York, NY: Cambridge University Press.
Sweller, J. (1994). “Cognitive load theory, learning difficulty, and instructional design”. Learning and instruction 4, 295-312. DOI: https://doi.org/10.1016/0959-4752(94)90003-5.
Van der Kolk, J. (2013). “Design of an electronic performance support system for food chemistry laboratory classes”. Thesis.
Van Merriënboer, J.J.G. and Kirschner, P.A. (2018). Ten steps to complex learning: A systematic approach to four-component instructional design. 2nd ed. New York, NY: Routledge.
Verstege, S. (2021). “Design of virtual experiment environments”. Thesis.
View the solutions that are based on this scientific background.
