Curriculum content—its fullness and actuality—is the purview of the curriculum author. Only he or she can specify which content fits the context of the course. Therefore, the quality of the content depends, somewhat, on how competent the subject matter expert is, but the rest is up to the curriculum author. However, the way of organizing and sequencing the content, its delivery and presentation, can be predefined in a standard way. For example, a model can be constructed depicting relations between desired pedagogies in the course, content, and technologies that support content delivery.
Hierarchical curricula have been the norm since Jerome Bruner. However, the existence of hierarchically structured representations has been fiercely contested by connectionism, dynamical systems theory, and related approaches. That they improve learning tractability is a strong reason for thinking hierarchical representations will develop during skill acquisition. There is also an inherent sense of fulfilling the purpose of developing a learning curriculum by infusing a sense of logical order of learning something into the structure of the content.
The next topic in a lesson or lesson in a course that should be logically included, however, is actually based on the goals and objectives of the instruction. According to Ng, Harda, and Russel (1999), the formula of P=S1+T>s2 (where S=state, T=Transitional Probabilities, and F=a function or an interaction that affects the probability of occurring) can be used to determine the next logical curriculum content choice (A.Y. Ng, D. Harada, & S. Russell (1999), Machine Learning – International Workshop Conference, Conference 16, U.S.: Morgan Kaufmann, 278-287). According to these authors, the only known variable in the equation above is the potential-based, shaping function. This function includes the potential of the learner to be able to apply the knowledge of the new material because of the instructional shaping method that was used.
To acquire and include this type of function in the curriculum, potential-based shaping rewards are required. More specifically, according to these authors, a subject must find a goal state at least once during exploration to continue. This implies that the motivation for a learner to choose a specific module or lesson is the change it brings in the learner’s goal state toward goal achievement. To attain maximum levels of motivation to complete the instruction, at least an approximation of the skill(s) to be learned needs to be included in the new lesson or module.
For declarative knowledge to be converted into procedural knowledge via procedural memory, there has to be some sort of “semi-controlled” practice before the “production” phase.
The distinction between declarative and procedural knowledge is not just a psychological dimension based on how knowledge is represented in memory. It is relevant to acquisition of new skills because of the need for transformation of knowledge between these two states in order to apply it to meet goals and sub-goals. Declarative representation (i.e., the knowledge of theoretical rules) means learners store knowledge in long-term memory as a database, which takes the form of a set of semantic networks and a general set of interpretative procedures (rules) to use the knowledge. When parts of the database are required to perform a certain operation, a set of general procedures is used (i.e., learners consciously apply the learned rule). Conversely, procedural knowledge (i.e., the ability to use the form correctly without being aware of the explicit rule) is embedded in procedures for action and not kept in a separate storage area. These procedures are activated when they match a proscribed pattern of sub-goal requirements.
The skill acquisition portion of procedural memory requires the development of a certain amount of automaticity. This automaticity results from an awareness of the components required to perform a task. According to R.W. Rhode, for Air Traffic Controllers, Situational Awareness is the three-dimensional picture of the sector and trafﬁc he has constructed in his brain. Controllers use Situational Awareness to assess the trafﬁc situation, project into the future, and make control decisions accordingly (R.W. Rhode R.W. (2017) Developing a Mental Model in ATC: 1—Learning Situational Assessment. In: Stanton N., Landry S., Di Bucchianico G., Vallicelli A. (eds.) Advances in Human Aspects of Transportation). In the case of ATC, according to R. Mogford, J. Guttman, S. Morrow, and P. Kopardekar, there appear to be two components of the controller’s mental model. The ﬁrst is a Domain Model that encompasses airspace, aircraft characteristics, and ATC procedures. The second factor is a Device Model, which is an understanding of the electronic systems (including the computer-human interface) designed to support ATC (R. Mogford, J. Guttman, S. Morrow, and P. Kopardekar (July 1995), The Complexity Construct in Air Trafﬁc Control: A Review and Synthesis of the Literature. DOT/FAA).
Both types of knowledge are essential if the air trafﬁc controller is to accomplish the task of separating and guiding aircraft. The Domain Mental Model is based on long-term memory, while the Device Model that provides the awareness of the immediate situation is based on short-term, or working, memory.
For declarative knowledge to be converted into procedural knowledge, there has to be some sort of “semi-controlled” practice before the “production” phase. For example, if the target activity is story-telling (narrative tenses), showing students pictures and asking them to describe what happened (with relatively few cues) during controlled practice might prove to be more effective than simply using a cloze exercise with 20 blanks and the base form of the verbs in parentheses (a course book favorite). In other words, not every kind of controlled practice is equally effective in terms of automaticity, the required condition for turning declarative knowledge into procedural knowledge. This book looks at how to determine what type of practice and how to tailor delivery mechanisms to the specific skill requirements of the learners.
Excerpt from “Prescriptive Curricula for Critical Thinking Skills” by Dr. Richard H. Vranesh (2019). For more information, visit: https://www.morebooks.shop/store/fr/book/prescriptive-curricula/isbn/978-620-0-45395-2
Doctor Richard Vranesh has been an instructional scientist working for the Federal Aviation Administration (FAA) for the last 20 years. For the FAA, he has assisted in the development of an eLMS for ATSS training, assisted in the selection of an authoring system for FAA Web-based training, made recommendations for SCORM compliance and the use of distance learning and Web-based training for air traffic and airway facilities personnel, and designed an integrated a training simulation system for the EnRoute controller that permits completing simulated scenarios on a suite of equipment that mirrors those used at the EnRoute Air Traffic Control Centers. He is currently providing guidance on the adoption of an LCMS for DOT-wide usage. He has taught at Temple University, Georgetown Medical School, and Prince George’s College. Dr. Vranesh has a doctorate in education from the Catholic University of America and a Master’s degree in curriculum development from the University of Northern Colorado. He is also the president and founder of the Capitol Chapter of the Association for Educational Communications and Technology. Dr. Vranesh is the author of “Prescriptive Curricula for Critical Thinking Skills.”