Executive SummaryAn important aspect of education is to promote higher-order thinking skills to learners. However, in the lecture environment, learners are passively engaged and it is unlikely for higher-order thinking to occur. Although interventions such as "clickers" can be used to increase engagement in lectures, this does not necessarily promote higher-order thinking. Approaches such as collaborative learning are better suited for this but there is little room to use such methods in the short time frame of a lecture.With recent advances in the capabilities of smart mobile devices and their growing penetration rate among the student cohort, it is possible to take advantage of these devices to design a system to promote higher-order thinking skills in the lecture environment.We present the design of a mobile-app-based collaborative learning system named myVote and a process for its usage. Our aim is to present a theoretical paper that discusses the relevant learning theories used in designing the system as well as describe a process to use the system to achieve collaborative learning at varying levels of thinking.We demonstrate the usefulness and flexibility of the system through three scenarios involving different levels of thinking, ranging from lower-to higher-order. Although the scenarios are in the context of IT education, the system is versatile enough to be adapted for education in general and also non-educational settings, such as business-like environments.Our contribution is a framework for using mobile apps and collaborative learning theories within a lecture environment to encourage higher-order thinking in learners.Although a potential limitation of the system is that it may not be appropriate for teaching more technical IT materials, such as programming and SQL code snippets, the problem can be recasted in a different format such as pseudocode in order to facilitate teaching in these areas.Keywords: higher-order thinking skills, collaborative learning, smartphones, computer-support collaborative learning, Delphi survey, mobile app IntroductionCollaborative learning is a group-based learning approach in which learners are mutually engaged in a coordinated fashion to achieve a learning goal or complete a learning task (Dillenbourg, Baker, Blaye, & O'Malley, 1996). Collaborative learning centers on a socialMaterial published as part of this publication, either on-line or in print, is copyrighted by the Informing Science Institute. Permission to make digital or paper copy of part or all of these works for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage AND that copies 1) bear this notice in full and 2) give the full citation on the first page. It is permissible to abstract these works so long as credit is given. To copy in all other cases or to republish or to post on a server or to redistribute to lists requires specific permission and payment of a fee. Contact Publisher@InformingScience.org to request redistri...

No abstract

Group-based learning creates an environment in which students can practice, gain, and improve soft skills such as leadership, communication, social, and conflict resolution skills. However, simply placing students in groups and creating group-based assessment tasks will not necessarily result in students developing and practicing these skills. Instead, specific approaches, such as cooperative learning in this case, need to be followed to ensure students develop theses skills. Of the six principles of cooperative learning, the first four (positive interdependence, accountability, team formation, and team size) can be addressed in the manner in which the groups are formed, its size, group assessment tasks, and the way in which the tasks are assessed. The remaining two principles, cognitive development and social development, can be addressed through a metacognitive approach to group supervision, which are detailed in this paper.

Abstract-Credit scoring has become a very important task in the credit industry and its use has increased at a phenomenal speed through the mass issue of credit cards since the 1960s. This paper compares the performance of current classifiers against an artificial intelligence technique based on the natural immune system, named simple artificial immune system (SAIS). Experiments were performed on three benchmark credit datasets and SAIS was found to be a very competitive classifier.

Abstract-Current artificial immune system (AIS) classifiers have two major problems: 1) their populations of B-cells can grow to huge proportions, and 2) optimizing one B-cell (part of the classifier) at a time does not necessarily guarantee that the B-cell pool (the whole classifier) will be optimized. In this paper, the design of a new AIS algorithm and classifier system called simple AIS is described. It is different from traditional AIS classifiers in that it takes only one B-cell, instead of a B-cell pool, to represent the classifier. This approach ensures global optimization of the whole system, and in addition, no population control mechanism is needed. The classifier was tested on seven benchmark data sets using different classification techniques and was found to be very competitive when compared to other classifiers.

No abstract

No abstract

Aim/Purpose: The COVID-19 pandemic quickly and suddenly affected many, if not all, parts of the world. From an educational perspective, educators and students managed the social restrictions by transitioning to teaching and learning completely online. As educators and students are two important stakeholders in education, the Special Series contains four articles that focus on these two perspectives.