We are backed by
the science of education

The e-learning solution provided by LabBuddy has been designed and is continuously improved based on scientific literature (Van der Kolk, 2013; Verstege, 2021). Here we provide some scientific background information related to our e-learning solution.

LabBuddy is based on

educational theories

All educational theories listed below are used to develop LabBuddy’s e-learning solution. Our scientists and developers continue to improve the solution using the newest insights in educational sciences and follow the latest developments in education technology.

 

4C/ID model

The ‘four-component instructional design’ (4C/ID) model (Van Merriënboer, 2018) addresses complex learning. The four components are as follows:

  • Learning tasks
  • Supportive information
  • Procedural information
  • Part-task practice

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 solution supports students in preparing for practical work beforehand so that they have already processed a lot of supportive information before entering the practical class. The e-learning tool will provide students with just-in-time information during practical 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 practical education.

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.

Multimedia Learning

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). Examples of these principles are focussing on directing the learner’s attention and minimizing unnecessary cognitive load.

Multimedia learning plays a significant role in LabBuddy’s e-learning solution, that is why many of Mayer’s principles were incorporated during its design.

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.

Cognitive Load Theory

Cognitive overload is one of the major problems during practical 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 solution was developed in such a way that it decreases the cognitive load imposed on students during practical work.

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.

Constructive Alignment Theory

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') practical classes this is often not the case, which, for example, results in students not preparing before they enter the practical class. When this is the case, it is usually worth applying the constructive alignment theory to the laboratory course.

LabBuddy’s e-learning solution provides students with novel learning activities (including preparation activities), making it easier for teachers to realize constructive alignment in their practical courses.

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.

ARCS model

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 solution 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 solution.

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.

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Kryt B.V., the company responsible for LabBuddy, operates an information security management system that complies with the requirements in ISO/IEC 27001:2022 for the development, delivery and support of e-learning software, as defined by management and in accordance with our statement of applicability. We will send you this statement of applicability on request.

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