A Framework for Online Teaching and Learning: The S-CARE Pedagogical Model

Student satisfaction and retention is a key feature of any good educational programme. Deden (2005) reports a 7.7 % improvement in student retention after one year through a number of measures including the quality of instructor’s online interaction. When measuring the success of an online programme a number of learning permutations have to be considered, namely: the learning activities, tools, resources and interactions or communication which makes up a pedagogical scenario/landscape. Daniel (2004), states that when designing and executing a pedagogical scenario the teacher has to respect a harmonic equilibrium between the freedom for intellectual development and motivation on one hand and certain guiding principles on the other. Putting all the above contentions together, this paper aims to present an analysis of the different pedagogical permutations exhibited by 7 different online facilitators in the Master of Instructional Design & Technology programme at the Open University Malaysia based on feedback from students, the facilitators and analysis of online interactions. This paper will present findings to the main research question that guided the study, namely, what are the main characteristics of an optimal pedagogical scenario employed by MIDT facilitators, and can these be translated into an online learning model? Findings showed that 4 major characteristics of an optimal online pedagogy were planning, interaction, feedback and focus. These 4 characteristics were further checked and analyzed with MIDT students and facilitators and as such a framework for online learning was developed into the S-CARE model. What is the S-CARE? It is a new online pedagogical model proposed in this work and it stands for S=Strategic, C=Consistent, A=Adaptive, R=Realistic and E=Effective. Initial results show that most facilitators exhibited some form of S-CARE, however the model will need further testing to ensure the suggested pedagogical permutations are applicable for most pure online courses. The success of the S-CARE portends well for the future in that it provides a structure to teaching and learning within the framework of chaos of the online environment. The combined experience of about 2 years of work shows both the potential and the way forward for the future and S-CARE is a step forward in helping online teaching and learning achieve its promise. (Abstract by authors

A Course Redesign Project: Adaptive Courseware in Biology

In the summer of 2019, a cooperative team of Biology faculty and a principal investigator worked to develop a solid set of aligned student learning outcomes across the sections of first semester (BIOL 1305) and second semester (BIOL 1306) of introductory Biology.  Additionally, the group worked on course objectives alignment within the scope and sequence of the courses, as well as aligned syllabi. A full course redesign was initiated over the summer, where the goal was to align student learning outcomes (SLOs), assessments, and develop a shared set of syllabi for six sections across two courses of introductory biology.  At UTEP, the overall goal was to integrate adaptive courseware technology tools, open education resources (OER) and active learning strategies within a course redesign in our Learning Management System (LMS), Blackboard, for a number of sections in Biology 1305 and Biology 1306 beginning in the spring of 2020. This is challenging, as much of adaptive courseware technology is not as strong in content as the Biology faculty would like for these classes, although it can help to substantially reduce the costs for students.  The information that follows defines the case study for integrating adaptive courseware within the course redesign process for a series of high enrollment introductory Biology course

Design of an E-learning system using semantic information and cloud computing technologies

Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí

Objectively Defining Scenario Complexity: Towards Automated, Adaptive Scenario-Based Training

Effective Scenario-Based Training (SBT) is sequenced in an efficient trajectory from novice to mastery and is well-grounded in pedagogically sound instructional strategies and learning theory. Adaptive, automated SBT attempts to sequence scenarios according to the performance of the student and implement the sequence without human agency. The source of these scenarios may take the form of a matrix constructed by Instructional Systems Designers (ISD), software engineers or trainers. The domain being instructed may contain procedures or concepts that are easily differentiated thus allowing quick and accurate determination of difficulty. In this instance, the sequencing of the SBT is relatively simple. However, in complex, domain-integrated instructional environments accurate and efficient sequencing may be extremely difficult as ISD, software engineers and trainers, without an objective means to calculate a scenario*s complexity must rely on subjectivity. In the Military, where time, fiscal and manpower constraints may lead to ineffective, inefficient and, perhaps, negative training SBT is a growing alternative to live training due to the significant cost avoidance demonstrated by such systems as the United States Marine Corps* (USMC) Abrams Main Battle Tank (M1A1) Advanced Gunnery Training System (AGTS). Even as the practice of simulation training grows, leadership such as the Government Accountability Office asserts that little has been done to demonstrate simulator impact on trainee proficiency. The M1A1 AGTS instructional sub system, the Improved Crew Training Program (ICTP), employs an automated matrix intended to increase Tank Commander (TC) and Gunner (GNR) team proficiency. This matrix is intended to guide the team along a trajectory of ever-increasing scenario difficulty. However, as designed, the sequencing of the matrix is based on subjective evaluation of difficulty, not on empirical or objective calculations of complexity. Without effective, automated SBT that adapts to the performance of the trainee, gaps in combat readiness and fiscal responsibility could grow large. In 2010, the author developed an algorithm intended to computationally define scenario complexity (Dunne, Schatz, Fiore, Martin & Nicholson, 2010) and conducted a proof of concept study to determine the algorithm*s effectiveness (Dunne, Schatz, Fiore, Nicholson & Fowlkes, 2010). Based on results of that study, and follow-on analysis, revisions were made to that Scenario Complexity (SC) algorithm. The purpose of this research was to examine the efficacy of the revised SC algorithm to enable Educators and Trainers, ISDs, and software engineers to objectively and computationally define SC. The research process included a period of instruction for Subject Matter Experts (SME) to receive instruction on how to identify the base variables that comprise SC. Using this knowledge SMEs then determined the values of the scenarios base variables. Once calculated, these values were ranked and compared to the ICTP matrix sequence. Results indicate that the SMEs were very consistent in their ratings of the items across scenario base variables. Due to the highly proceduralized process underlying advanced gunnery skills, this high degree of agreement was expected. However, the significant lack of correlation to the matrix sequencing is alarming and while a recent study has shown the AGTS to increase TC and GNR team proficiency (PM TRASYS, 2014a), this research*s findings suggests that redesign of the ICTP matrix is in order