Was it good for them? Reflections on the use of MENTOR and changes in course design using general performance measures

This paper considers the use of general performance measures in evaluating specific planning and design decisions in higher education and reflects on the students' learning process. Specifically, it concerns the use of the MENTOR multimedia computer aided learning package for helping students learn about OR as part of a general business degree. It includes the transfer of responsibility for a learning module to a new staff member and a change from a single tutor to a system involving multiple tutors. Student satisfaction measures, learning outcome measures and MENTOR usage patterns are examined in monitoring the effects of the changes in course delivery. The results raise some questions about the effectiveness of general performance measures in supporting specific decisions relating to course design and planning.

Collaborative design of assessment criteria to improve undergraduate student engagement and performance

Student engagement is vital in enhancing the student experience and encouraging deeper learning. Involving students in the design of assessment criteria is one way in which to increase student engagement. In 2011, a marking matrix was used at Aston University (UK) for logbook assessment (Group One) in a project-based learning module. The next cohort of students in 2012 (Group Two) were asked to collaboratively redesign the matrix and were given a questionnaire about the exercise. Group Two initially scored a lower average logbook mark than Group One. However, Group Two showed the greatest improvement between assessments, and the quality of, and commitment to, logbooks was noticeably improved. Student input resulted in a more defined, tougher mark scheme. However, this provided an improved feedback system that gave more scope for self-improvement. The majority of students found the exercise incorporated their ideas, enhanced their understanding, and was useful in itself.

Bilingual asynchronous online discussion groups : design and delivery of an e-Learning distance study module for nurse academics in a developing country

The advent of eLearning has seen online discussion forums widely used in both undergraduate and postgraduate nursing education. This paper reports an Australian university experience of design, delivery and redevelopment of a distance education module developed for Vietnamese nurse academics. The teaching experience of Vietnamese nurse academics is mixed and frequently limited. It was decided that the distance module should attempt to utilise the experience of senior Vietnamese nurse academics - asynchronous online discussion groups were used to facilitate this. Online discussion occurred in both Vietnamese and English and was moderated by an Australian academic working alongside a Vietnamese translator. This paper will discuss the design of an online learning environment for foreign correspondents, the resources and translation required to maximise the success of asynchronous online discussion groups, as well as the rationale of delivering complex content in a foreign language. While specifically addressing the first iteration of the first distance module designed, this paper will also address subsequent changes made for the second iteration of the module and comment on their success. While a translator is clearly a key component of success, the elements of simplicity and clarity combined with supportive online moderation must not be overlooked.

The TKT Teaching Knowledge Test. course : CLIL module ( Content and Language Integrate Learning).

El TKT es un examen que se centra en los conocimientos esenciales que requiere todo profesor de inglés como segunda lengua. Evalúa los conocimientos pedagógicos y no las habilidades didácticas. Quienes se preparan para él profundizan sus conocimientos sobre la enseñanza del inglés y mejoran su comprensión de los conceptos relacionados con la lengua y su uso, así como la teoría y la práctica del proceso de enseñanza y aprendizaje. La prueba es adecuada para todos los profesores de Aprendizaje Integrado de Contenidos y Lenguas Extranjeras (AICLE) que trabajan en primaria y secundaria. Cubre las cuatro áreas claves de la prueba: conocimiento y principios de AICLE, preparación de la lección, entrega de la lección y evaluación La prueba también es adecuada para los profesores que trabajan en inglés como segunda lengua (EAL). Es una prueba de ochenta minutos que cuenta con ochenta preguntas. Éstas son una mezcla de distintas opciones verdadero - falso, selección múltiple, etc.

The development of an on-line module for students on “Working in Inter-cultural Teams”

Since 2005 QUT through a number of large Teaching and Learning Grants has sponsored a range of teamwork learning initiatives to assist students to develop the teamwork skills demanded by industry. After a suite of six online team learning modules was developed, first year unit coordinators requested an additional module to address the challenges of working with the diverse range of social, cultural and personal values that students from different backgrounds bring to student teams. The Intercultural Teams module asks students to map themselves against a Cultural Orientations Framework so they can understand their own cultural beliefs. By learning about other cultural orientations and comparing and analysing their effects, team members can develop communication and team process management strategies to leverage their differences to realise effective and creative outcomes. The interactive session will demonstrate the elements of the Intercultural Teams module and ask participants to consider ways the module can be integrated into classroom learning to support the development of students’ intercultural competencies.

The nonconceptual gateway to early word learning

My research investigates why nouns are learned disproportionately more frequently than other kinds of words during early language acquisition (Gentner, 1982; Gleitman, et al., 2004). This question must be considered in the context of cognitive development in general. Infants have two major streams of environmental information to make meaningful: perceptual and linguistic. Perceptual information flows in from the senses and is processed into symbolic representations by the primitive language of thought (Fodor, 1975). These symbolic representations are then linked to linguistic input to enable language comprehension and ultimately production. Yet, how exactly does perceptual information become conceptualized? Although this question is difficult, there has been progress. One way that children might have an easier job is if they have structures that simplify the data. Thus, if particular sorts of perceptual information could be separated from the mass of input, then it would be easier for children to refer to those specific things when learning words (Spelke, 1990; Pylyshyn, 2003). It would be easier still, if linguistic input was segmented in predictable ways (Gentner, 1982; Gleitman, et al., 2004) Unfortunately the frequency of patterns in lexical or grammatical input cannot explain the cross-cultural and cross-linguistic tendency to favor nouns over verbs and predicates. There are three examples of this failure: 1) a wide variety of nouns are uttered less frequently than a smaller number of verbs and yet are learnt far more easily (Gentner, 1982); 2) word order and morphological transparency offer no insight when you contrast the sentence structures and word inflections of different languages (Slobin, 1973) and 3) particular language teaching behaviors (e.g. pointing at objects and repeating names for them) have little impact on children's tendency to prefer concrete nouns in their first fifty words (Newport, et al., 1977). Although the linguistic solution appears problematic, there has been increasing evidence that the early visual system does indeed segment perceptual information in specific ways before the conscious mind begins to intervene (Pylyshyn, 2003). I argue that nouns are easier to learn because their referents directly connect with innate features of the perceptual faculty. This hypothesis stems from work done on visual indexes by Zenon Pylyshyn (2001, 2003). Pylyshyn argues that the early visual system (the architecture of the "vision module") segments perceptual data into pre-conceptual proto-objects called FINSTs. FINSTs typically correspond to physical things such as Spelke objects (Spelke, 1990). Hence, before conceptualization, visual objects are picked out by the perceptual system demonstratively, like a finger pointing indicating ‘this’ or ‘that’. I suggest that this primitive system of demonstration elaborates on Gareth Evan's (1982) theory of nonconceptual content. Nouns are learnt first because their referents attract demonstrative visual indexes. This theory also explains why infants less often name stationary objects such as plate or table, but do name things that attract the focal attention of the early visual system, i.e., small objects that move, such as ‘dog’ or ‘ball’. This view leaves open the question how blind children learn words for visible objects and why children learn category nouns (e.g. 'dog'), rather than proper nouns (e.g. 'Fido') or higher taxonomic distinctions (e.g. 'animal').

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

Intelligent tutoring system for learning PHP

This thesis investigates the possibility of using an adaptive tutoring system for beginning programming students. The work involved, designing, developing and evaluating such a system and showing that it was effective in increasing the students’ test scores. In doing so, Artificial Intelligence techniques were used to analyse PHP programs written by students and to provide feedback based on any specific errors made by them. Methods were also included to provide students with the next best exercise to suit their particular level of knowledge.

Incorporating anchored learning in a C# intelligent tutoring system

This research showed that one solution that can be used to help the students learn how to program is by providing a system that can behave like a tutor to teach the students individually. An intelligent tutoring system named CSTutor was built in this research to assist the students. CSTutor asks the student to write programs in a role playing environment, presenting the most appropriate tasks to the students, and provides help to the students' problems.

Selection procedures for module discovery : exploring evolutionary algorithms for cognitive science

Evolutionary algorithms are playing an increasingly important role as search methods in cognitive science domains. In this study, methodological issues in the use of evolutionary algorithms were investigated via simulations in which procedures were systematically varied to modify the selection pressures on populations of evolving agents. Traditional roulette wheel, tournament, and variations of these selection algorithms were compared on the “needle-in-a-haystack” problem developed by Hinton and Nowlan in their 1987 study of the Baldwin effect. The task is an important one for cognitive science, as it demonstrates the power of learning as a local search technique in smoothing a fitness landscape that lacks gradient information. One aspect that has continued to foster interest in the problem is the observation of residual learning ability in simulated populations even after long periods of time. Effective evolutionary algorithms balance their search effort between broad exploration of the search space and in-depth exploitation of promising solutions already found. Issues discussed include the differential effects of rank and proportional selection, the tradeoff between migration of populations towards good solutions and maintenance of diversity, and the development of measures that illustrate how each selection algorithm affects the search process over generations. We show that both roulette wheel and tournament algorithms can be modified to appropriately balance search between exploration and exploitation, and effectively eliminate residual learning in this problem.