Professional training and learning during Covid

The need for scenario based training

Professional training and learning during Covid

May 21 2020
Quick Summary:
  • Mazetec was build for training in dynamic high-pressure situations.
  • There are many jobs that require making difficult decisions under pressure; however, there are few where someone’s life is at stake.
  • Mazetec helps you teach in a distracted world
  • MOOCs are great at recreating the classroom but that's the problem.
  • Online learning in the age of Covid needs a different approach

Why we need scenario based training and a new way to learn online.

Remote and distance learning used to only be a problem for rural areas, now it's a problem for everyone. The economic, social, and health benefits of achieving higher education and practicing lifelong learning are widely researched, known, and accepted. It is also known what a disadvantage it is to have limited access to educational resources and training opportunities.

Distance education enrollment steadily rose over the last 14 years to roughly 30% of all college and university students, but shock of Covid-19 has pushed it to 90%. Learning management systems (LMS) have slowly evolved to meet the demands of students and content authors, but now are stretched beyond their limits thwarted with bottlenecks and barriers. It is convenient for adult learners who have full time jobs and challenging schedules, if the learning tasks are clear. The primary types of online learning environments are synchronous and asynchronous, or a combination of both.

Many of the synchronous online learning systems attempt to recreate the classroom experience with a live instructor lecturing over a video feed at a set date and time. In synchronous systems, students interact in real-time with their instructor and fellow students. The asynchronous learning systems enable on-demand learning, where the learner consumes the content – e.g., text, video, or slides – at a self-determined pace, location, and time.

(MOOCs (massive open online courses))[] attempt to recreate the classroom experience. Coursera, a MOOC provider, boasts having as many as 120,000 students enroll in a single course. Yet the average MOOC completion rate stands at 13%; that’s an 87% dropout rate. This speaks volumes about student engagement in the “one size fits all” MOOC format. A four-year, E.U.-commissioned case study on MOOCs at France Université Numérique (FUN) from October 2013 - July 2015 found that out of 1,800,000 enrolled students across 140 courses offered through 50 different educational institutions, the rate of MOOC completion of the courses was about 10%. Simply recreating the classroom experience online is no longer a novel approach and carries with it the same limitations that online learning was supposedly going to solve, such as adapting the learning to individual needs.

Nearly all on demand and self-service learning is passive, because the learner's experience is passive and disconnected from the reason why they are learning. Most online learning and distance education courses today are asynchronous and present information in what the instructor determines to be the most logical order, often from beginning to end. The learner's opportunities to interact with the content are limited to quizzes, assigned homework, or references to external content. Student grades are the ultimate determinant of how well the content of an online course was organized, applicable to the students, and clearly communicated.

In these asynchronous passive learning environments, the action of learning is separated from the action of doing. According to the educational theory of constructivism discussed in the literature review the best way to learn is to practice in the moment so the learner receives immediate feedback on what he or she is learning, understanding, and comprehending. A passive course may demotivate learners if they don't immediately relate to the importance of the content or how it applies to their area of interest. Learner disinterest may initiate a pattern of disengagement, poor performance, and failure to complete the training.

Before Mazetec it was challenging to teach case-based scenarios in high-pressure domains in online learning environments. Case-based scenarios in high pressure domains includes areas such as medicine, call center work, and suicide prevention. For example, the job of a suicide crisis line volunteer may include the following scenario:

The phone rings, you answer, the caller is crying. They called to decide whether life is worth living or to die by suicide. You have 60 seconds to talk them down and if you say the wrong thing, the caller may die by suicide. What do you say?

There are many jobs that require making difficult decisions under pressure; however, there are few where someone’s life is at stake. Suicide prevention training is required for health care practitioners in 29 states, but while the certificate satisfies the legal requirement, it is the training that enables the health care workers to save lives.

This method of instruction is called Constructivism [6], and it is based on the belief that efficient learning occurs when learners are actively challenged to simultaneously understand and apply what they just learned to a simulated challenge, followed by immediate performance feedback as opposed to passively viewing or reading information. This is related to research [7] on state-dependent memory that has found that if the task is performed under pressure, training should be conducted under pressure. However, there are no generic distance education learning management systems today that uses both a dynamic constructivist approach that are able to simulate pressure and deliver the course in an unsupervised asynchronous manner.

This thesis presents introduces Mazetec, an adaptive scenario-based e-learning platform. Mazetec implements a state-dependent, constructivist, pedagogical approach to online learning. It enables subject matter experts (SMEs) to create interactive, state-dependent, case studies or courses with branching logic for online learning and knowledge testing. Mazetec is a complex web application designed to deliver decision-based or case-based educational scenarios and simulations in a time-unlimited or time-limited, non-linear, online format for high-pressure domains, such as crisis lines, emergency medicine, police, crisis management, or cyber security. A learner must navigate to the correct end of a learning maze within the set time limitation or not. The non-linear flexibility to create many different concurrent paths and outcomes in a course allows SMEs to create teachable moments by validating a learner's incorrect assumptions and presenting them with the consequences of their actions. In addition, the platform collects a significant number of useful analytics, such as overall completion time, the path of decisions, and time to make each decision, and allows the SME to replay each user's attempt as well as aggregate the analytics for each maze.

It is well-understood that most organizations and institutions that require online learning systems are locked into those solutions. The system presented in this paper was designed to work with these systems to allow SMEs to fill a gap overlooked by commercial vendors in distance education delivery. Mazetec can serve as a standalone training system or as an integrated supplementary activity provider to an existing course in an organization's existing LMS. Mazetec is working toward xAPI compliance to easily integrate with existing LMSs and learning record stores (LRS). 

Mazetec is currently being used by (ICF)[] Management consulting company, in conducting a study to better understand how the use of active learning strategies (the Mazetec software) teach the (QPR)[] suicide mitigation intervention affects trainee knowledge and skills retention over time. Specifically, the study sought to determine if the Mazetec “interventions following training participation increase the effectiveness of gatekeeper [suicidal behavior identification and mitigation] trainings, particularly in terms of promoting identification of at-risk youth”. Although the results of this study are not yet available, preliminary data analysis indicates evidence of user engagement and demonstrates the utility of the various analytics collected by the Mazetec system.

Subsequent posts will address

  1. How can we engineer a learning system to build, manage, and deliver unsupervised branching case-based scenarios to simulate a high-pressure situation that a learner must resolve such as suicidal person in crisis and the learner talking them down?
  2. Will educators and learners find value in a scenario-based learning system?
  3. Does the use of these interactive scenario interventions during or following training participation increase effectiveness of the trainings?

Cero, I., & Witte, T. K. (2020). Assortativity of suicide-related posting on social media. American Psychologist, 75(3), 365–379.

Lally, P., Jaarsveld, M., Potts, H., & Wardel, J. (2010). How are habits formed: Modeling habit formation in the real world. European Journal of Social Psychology, 40, 9980-1009. DOI: 10.1002/ejsp.674

Morris, Z.S., (2011). The answer is 17 years, what is the question: understanding time lags in translational research. Journal of the Royal Society of Medicine.

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