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...

Fuzzy logic controllers (FLCs) are gaining in popularity across a broad array of disciplines because they allow a more human approach to control. Recently, the design of the fuzzy sets and the rule base has been automated by the use of genetic algorithms (GAs) which are powerful search techniques. Though the use of GAs can produce near optimal FLCs, it raises problems such as messy overlapping of fuzzy sets and rules not in agreement with common sense. This paper describes an enhanced genetic algorithm which constrains the optimization of FLCs to produce well-formed fuzzy sets and rules which can be better understood by human beings. To achieve the above, we devised several new genetic operators and used a parallel GA with three populations for optimizing FLCs with 3x3, 5x5, and 7x7 rule bases, and we also used a novel method for creating migrants between the three populations of the parallel GA to increase the chances of optimization. In this paper, we also present the results of applying our GA to designing FLCs for controlling three different plants and compare the performance of these FLC's with their unconstrained counterparts.

Applying the engaging and motivating aspects of video games in non-game contexts is known as gamification. Education can benefit from gamification by improving the learning environment to make it more enjoyable and engaging for students. Factors that influence students’ preference for use of gamification are identified. Students are surveyed on their experiences of playing a gamified quiz, named Quick Quiz, during class. Quick Quiz features several gamification elements such as points, progress bars, leader boards, timers, and charts. Data collected from the survey is analysed using Partial Least Squares. Factors including ‘usefulness’, ‘preference for use’, ‘knowledge improvement’, ‘engagement’, ‘immersion’ and ‘enjoyment’ were found to be significant determinants.  Students were found to have a preference for use for gamification in their learning environment.

Executive SummaryWhile delivering the Intelligent Systems course, an elective course in the Master of Business Information Technology program at RMIT University, it was felt that there was a learning issue as students' learning seemed to be superficial. This perception was based on the questions students asked in class and the mechanical attitude they adopted while doing lab work. In the next version of the course, it was decided to trial a problem-based learning (PBL) teaching and learning approach in order to improve students' learning experience.PBL is a revolutionary and radical teaching approach. It is completely different from the traditional lecture-tutorial approach as there is a shift of power from the "expert teacher" to the "student learner". In the traditional teacher-centered approach, the teacher is knowledgeable in the subject matter and the focus of teaching is on the transmission of knowledge from the expert teacher to the novice student. In contrast, the PBL approach is a student-centered approach in which the focus is on student's learning and what they do to achieve this. In such an environment, the role of the teacher is more of a facilitator than an instructor.In order to use PBL effectively, it is important to understand how it is grounded in current theories of teaching and learning so that insights from these theories can be applied to refine the practice of PBL. Findings from cognitive psychology suggest that learning is a constructive and not a receptive process. Cognitive processes known as metacognition affect the use of knowledge, and social and contextual factors affect learning. When educating students, explicit attention should be paid to their existing knowledge to provide them a framework for learning. Learning is quicker when students possess self-monitoring skills known as metacognition (i.e. the student's ability to analyze, reflect on, and understand his or her own cognitive and learning processes). Metacognitive skills allow students to monitor their own learning and, contrary to prior beliefs, they can be taught. Learning must be contextualized in order to be effective and social factors also affect learning since students evolve their problem-solving methods and conceptual knowledge when working in small groups.When using the PBL approach, it is necessary for students to follow a carefully planned process to guide them through the complex tasks of brainstorming, identifying useful knowledge, formulatMaterial 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 redistri...

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.

The purpose of this study is to examine the combined effect of selfefficacy and academic integration on higher education students studying IT (Information Technology) majors in Taiwan. We introduced self-efficacy, which is a psychological factor that affects students' academic outcomes, as a new factor in Tinto' theory, a well-known framework in student retention research. Academic integration is the main proposition of Tinto's theory affecting students' decision to dropout. Students from different populations have various reasons from dropping out of their studies. An examination of the relationship between self-efficacy and academic integration is useful to understand the effect of self-efficacy on academic outcomes on the IT student population in Taiwan. Data from a Taiwanese national survey database conducted in 2005 was used to achieve the research objective. A total of 2,895 records were extracted from 75,084 students in public and private institutions studying in two IT-related Majors, namely Information Management (IM) and Computer Science (CS). MANOVA was used to analyze the interaction effects between academic integration and self-efficacy. The independent variables were institution types and students' majors. The results showed that students from public institutions have higher levels of self-efficacy than students from private ones. Another finding is that IM students seem to have better study strategies and habits than CS students. However, CS students were found to have better collaboration and satisfaction with their institutions than IM students. Team projects, counselling Educ Inf Technol (2010) 15:333-353 services, and flexible teaching and learning strategy are suggested to enhance students' academic integration and self-efficacy.

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.

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