Learning from Incredible Commitments: Evolution and Impact of Bilateral Investment Treaties

Ostensibly, BITs are the ideal international treaty. First, until just recently, they almost uniformly came with explicit dispute resolution mechanisms through which countries could face real costs for violation (Montt 2009). Second, the signing, ratification, and violation of them are easily accessible public knowledge. Thus countries presumably would face reputational costs for violating these agreements. Yet, these compliance devices have not dissuaded states from violating these agreements. Even more interestingly, in recent years, both developed and developing countries have moved towards modifying the investor-friendly provisions of these agreements. These deviations from the expectations of the credible commitment argument raise important questions about the field's assumptions regarding the ability of international treaties with commitment devices to effectively constrain state behavior.

Learning and Exploiting Camera Geometry for Computer Vision

This work explores the use of statistical methods in describing and estimating camera poses, as well as the information feedback loop between camera pose and object detection. Surging development in robotics and computer vision has pushed the need for algorithms that infer, understand, and utilize information about the position and orientation of the sensor platforms when observing and/or interacting with their environment.

The first contribution of this thesis is the development of a set of statistical tools for representing and estimating the uncertainty in object poses. A distribution for representing the joint uncertainty over multiple object positions and orientations is described, called the mirrored normal-Bingham distribution. This distribution generalizes both the normal distribution in Euclidean space, and the Bingham distribution on the unit hypersphere. It is shown to inherit many of the convenient properties of these special cases: it is the maximum-entropy distribution with fixed second moment, and there is a generalized Laplace approximation whose result is the mirrored normal-Bingham distribution. This distribution and approximation method are demonstrated by deriving the analytical approximation to the wrapped-normal distribution. Further, it is shown how these tools can be used to represent the uncertainty in the result of a bundle adjustment problem.

Another application of these methods is illustrated as part of a novel camera pose estimation algorithm based on object detections. The autocalibration task is formulated as a bundle adjustment problem using prior distributions over the 3D points to enforce the objects' structure and their relationship with the scene geometry. This framework is very flexible and enables the use of off-the-shelf computational tools to solve specialized autocalibration problems. Its performance is evaluated using a pedestrian detector to provide head and foot location observations, and it proves much faster and potentially more accurate than existing methods.

Finally, the information feedback loop between object detection and camera pose estimation is closed by utilizing camera pose information to improve object detection in scenarios with significant perspective warping. Methods are presented that allow the inverse perspective mapping traditionally applied to images to be applied instead to features computed from those images. For the special case of HOG-like features, which are used by many modern object detection systems, these methods are shown to provide substantial performance benefits over unadapted detectors while achieving real-time frame rates, orders of magnitude faster than comparable image warping methods.

The statistical tools and algorithms presented here are especially promising for mobile cameras, providing the ability to autocalibrate and adapt to the camera pose in real time. In addition, these methods have wide-ranging potential applications in diverse areas of computer vision, robotics, and imaging.

Effects of Altered Prenatal Sensory Stimulation on Postnatal Contingency Learning in Bobwhite Quail Neonates (Colinus Virginianus)

Preterm infants are exposed to high levels of modified early sensory experience in the Neonatal Intensive Care Unit (NICU). Reports that preterm infants show deficits in contingency detection and learning when compared to full-term infants (Gekoski, Fagen, & Pearlman, 1984; Haley, Weinberg, & Grunau, 2006) suggest that their exposure to atypical amounts or types of sensory stimulation might contribute to deficits in these critical skills. Experimental modifications of sensory experience are severely limited with human fetuses and preterm infants, and previous studies with precocial bird embryos that develop in ovo have proven useful to assess the effects of modified perinatal sensory experience on subsequent perceptual and cognitive development. In the current study, I assessed whether increasing amounts of prenatal auditory or visual stimulation can interfere with quail neonates’ contingency detection and contingency learning in the days following hatching. Results revealed that augmented prenatal visual stimulation prior to hatching does not disrupt the ability of bobwhite chicks to recognize and prefer information learned in a contingent fashion, whereas augmented prenatal auditory stimulation disrupted the ability of chicks to benefit from contingently presented information. These results suggest that specific types of augmented prenatal stimulation that embryos receive during late prenatal period can impair the ability to learn and remember contingently presented information. These results provide testable developmental hypotheses, with the goal of improving the developmental care and management of preterm neonates in the NICU setting.

The Optimality of Decision Making During Motor Learning

In our daily lives, we often must predict how well we are going to perform in the future based on an evaluation of our current performance and an assessment of how much we will improve with practice. Such predictions can be used to decide whether to invest our time and energy in learning and, if we opt to invest, what rewards we may gain. This thesis investigated whether people are capable of tracking their own learning (i.e. current and future motor ability) and exploiting that information to make decisions related to task reward. In experiment one, participants performed a target aiming task under a visuomotor rotation such that they initially missed the target but gradually improved. After briefly practicing the task, they were asked to select rewards for hits and misses applied to subsequent performance in the task, where selecting a higher reward for hits came at a cost of receiving a lower reward for misses. We found that participants made decisions that were in the direction of optimal and therefore demonstrated knowledge of future task performance. In experiment two, participants learned a novel target aiming task in which they were rewarded for target hits. Every five trials, they could choose a target size which varied inversely with reward value. Although participants’ decisions deviated from optimal, a model suggested that they took into account both past performance, and predicted future performance, when making their decisions. Together, these experiments suggest that people are capable of tracking their own learning and using that information to make sensible decisions related to reward maximization.

Differential ability and attainment in language and arithmetic of Dutch primary school pupils.

Background. In pre-school and primary education pupils differ in many abilities and competences (‘giftedness’). Yet mainstream educational practice seems rather homogeneous in providing age-based or grade-class subject matter approaches. Aims. To clarify whether pupils scoring initially at high ability level do develop and attain differently at school with respect to language and arithmetic compared with pupils displaying other initial ability levels. To investigate whether specific individual, family or educational variables co-vary with the attainment of these different types of pupils in school. Samples. Data from the large-scale PRIMA cohort study including a total of 8258 grade 2 and 4 pupils from 438 primary schools in The Netherlands. Methods. Secondary analyses were carried out to construct gain scores for both language and arithmetic proficiency and a number of behavioural, attitudinal, family and educational characteristics. The pupils were grouped into different ability categories (highly able; able; above average; average and below). Further analyses used Pearson correlations and analyses of variance both between and within ability categories. Cross-validation was done by introducing a cohort of younger pupils in pre-school and grouping both cohorts into decile groups based on initial ability in language and arithmetic. Results. Highly able pupils generally decreased in attainment in both language and arithmetic, whereas pupils in average and below average groups improved their language and arithmetic scores. Only with highly able pupils were some educational characteristics correlated with the pupils’ development in achievement, behaviour and attitudes. Conclusions. Pre-school and primary education should better match pupils’ differences in abilities and competences from their start in pre-school to improve their functioning, learning processes and outcomes. Recommendations for educational improvement strategies are presented in closing.

Learning for self-regulation: Improving instructional benefits for pupils, teachers, parents, schools, and society at large

Compulsory education laws oblige primary and secondary schools to give each pupil positive encouragement in, for example, social, emotional, cognitive, creative, and ethical respects. This is a fairly smooth process for most pupils, but it is not as easy to achieve with others. A pattern of pupil, home or family, and school variables turns out to be responsible for a long-term process that may lead to a pupil’s dropping out of education. A systemic approach will do much to introduce more clarity into the diagnosis, potential reduction and possible prevention of some persistent educational problems that express themselves in related phenomena, for example low school motivation and achievement; forced underachievement of high ability pupils; concentration of bullying and violent behaviour in and around some types of classes and schools; and drop-out percentages that are relatively constant across time. Such problems have a negative effect on pupils, teachers, parents, schools, and society alike. In this address, I would therefore like to clarify some of the systemic causes and processes that we have identified between specific educational and pupil characteristics. Both theory and practice can assist in developing, implementing, and checking better learning methods and coaching procedures, particularly for pupils at risk. This development approach will take time and require co-ordination, but it will result in much better processes and outcomes than we are used to. First, I will diagnose some systemic aspects of education that do not seem to optimise the learning processes and school careers of some types of pupils in particular. Second, I will specify cognitive, social, motivational, and self-regulative aspects of learning tasks and relate corresponding learning processes to relevant instructional and wider educational contexts. I will elaborate these theoretical notions into an educational design with systemic instructional guidelines and multilevel procedures that may improve learning processes for different types of pupils. Internet-based Information and Communication Technology, or ICT, also plays a major role here. Third, I will report on concrete developments made in prototype research and trials. The development process concerns ICT-based differentiation of learning materials and procedures, and ICT-based strategies to improve pupil development and learning. Fourth, I will focus on the experience gained in primary and secondary educational practice with respect to implementation. We can learn much from such practical experience, in particular about the conditions for developing and implementing the necessary changes in and around schools. Finally, I will propose future research. As I hope to make clear, theory-based development and implementation research can join forces with systemic innovation and differentiated assessment in educational practice, to pave the way for optimal “learning for self-regulation” for pupils, teachers, parents, schools, and society at large.

Bespoke Radiology e-Learning

Introduction: Foundation doctors are expected to assess and interpret plain x-ray studies of the chest/abdomen before a definitive report is issued by senior staff. The Royal College of Radiologists have published guidelines (RCR curriculum) on the scope of plain film findings medical students should be familiar with.1 Studies have shown that the x-ray interpretation without feedback does not significantly improve diagnostic ability. 2 Queen’s University, Belfast Trust Radiology and Experior Medical developed an online system to assess individual student ability to interpret X-ray findings. Over a series of assessments each student’s profile is built up, identifying strengths and weakness. The system can then create bespoke individual assessments re-evaluating previously identified weak areas and quantifying interpretative skill improvement. Aim: To determine how readily an online system is adopted by senior medical students, investigating if increasing exposure to x-ray interpretation combined with cyclical formative feedback enhances performance. Methods: The system was offered to all 270 final year medical students as an online resource. The system comprised a series of 20 weekly 30 minute assessments, containing normal and abnormal x-rays within the RCR curriculum. After each assessment students were given formative feedback, including their own result, annotated answers, peer group comparison and a breakdown of areas of strength and weakness. Focus groups of 4-5 students addressed student perspectives of the system, including ease of use, image resolution, system performance across different operating platforms, perceived value of formative feedback loops, breakdown of performance and the value of bespoke personalised assessments. Research Ethics Approval was granted for the study. Data analysis was via two-sided one-sample t-test; initial minimal recruitment was estimated as 60 students, to detect a mean 10% change in performance, with a standard deviation of 20%. Results and Discussion: Over 80% (n = XXX/270) of the student cohort engaged with the study. Student baseline average was 39%, increasing to 62% by the exit test. The steadily sustained improvement (57% relative performance in interpretative diagnostic accuracy) was despite increasing test difficulty. Student feedback via focus groups was universally positive throughout the examined domains. Conclusion: The online resource proved to be valuable, with high levels of student engagement, improving performance despite increasingly difficulty testing and positive learner experience with the system. References: 1. Undergraduate Radiology Curriculum, The Royal College of Ra, April 2012. Ref No. BFCR(12)4 The Royal College of Radiologists, April 2012 2. I Satia, S Bashagha, A Bibi, R Ahmed, S Mellor, F Zaman. Assessing the accuracy and certainty in interpretating chest x-rays in the medical division. Clin Med August 2013 Vol.13 no. 4 349-352

A Biocultural Examination of Student Learning Behaviors in Large Undergraduate Lectures

Thesis (Ph.D.)--University of Washington, 2016-08

Professional Practice in Learning and Development: How to Design and Deliver Plans for the Workplace

Introduction The world is changing! It is volatile, uncertain, complex and ambiguous. As cliché as it may sound the evidence of such dynamism in the external environment is growing. Business-as-usual is more of the exception than the norm. Organizational change is the rule; be it to accommodate and adapt to change, or instigate and lead change. A constantly changing environment is a situation that all organizations have to live with. What makes some organizations however, able to thrive better than others? Many scholars and practitioners believe that this is due to the ability to learn. Therefore, this book on developing Learning and Development (L&D) professionals is timely as it explores and discusses trends and practices that impact organizations, the workforce and L&D professionals. Being able to learn and develop effectively is the cornerstone of motivation as it helps to address people’s need to be competent and to be autonomous (Deci & Ryan, 2002; Loon & Casimir, 2008; Ryan & Deci, 2000). L&D stimulates and empowers people to perform. Organizations that are better at learning at all levels; the individual, group and organizational level, will always have a better chance of surviving and performing. Given the new reality of a dynamic external environment and constant change, L&D professionals now play an even more important role in their organizations than ever before. However, L&D professionals themselves are not immune to the turbulent changes as their practices are also impacted. Therefore, the challenges that L&D professionals face are two-pronged. Firstly, in relation to helping and supporting their organization and its workforce in adapting to the change, whilst, secondly developing themselves effectively and efficiently so that they are able to be one-step ahead of the workforce that they are meant to help develop. These challenges are recognised by the CIPD, as they recently launched their new L&D qualification that has served as an inspiration for this book. L&D plays a crucial role at both strategic (e.g. organizational capability) and operational (e.g. delivery of training) levels. L&D professionals have moved from being reactive (e.g. following up action after performance appraisals) to being more proactive (e.g. shaping capability). L&D is increasingly viewed as a driver for organizational performance. The CIPD (2014) suggest that L&D is increasingly expected to not only take more responsibility but also accountability for building both individual and organizational knowledge and capability, and to nurture an organizational culture that prizes learning and development. This book is for L&D professionals. Nonetheless, it is also suited for those studying Human Resource Development HRD at intermediate level. The term ‘Human Resource Development’ (HRD) is more common in academia, and is largely synonymous with L&D (Stewart & Sambrook, 2012) Stewart (1998) defined HRD as ‘the practice of HRD is constituted by the deliberate, purposive and active interventions in the natural learning process. Such interventions can take many forms, most capable of categorising as education or training or development’ (p. 9). In fact, many parts of this book (e.g. Chapters 5 and 7) are appropriate for anyone who is involved in training and development. This may include a variety of individuals within the L&D community, such as line managers, professional trainers, training solutions vendors, instructional designers, external consultants and mentors (Mayo, 2004). The CIPD (2014) goes further as they argue that the role of L&D is broad and plays a significant role in Organizational Development (OD) and Talent Management (TM), as well as in Human Resource Management (HRM) in general. OD, TM, HRM and L&D are symbiotic in enabling the ‘people management function’ to provide organizations with the capabilities that they need.

Learning from Text: Examining Teacher Thinking and Practice in AP Environmental Science

Thesis (Ph.D.)--University of Washington, 2016-08

Convex Optimization Algorithms and Statistical Bounds for Learning Structured Models

Thesis (Ph.D.)--University of Washington, 2016-08

Teaching down or learning up

Résumé : L'élément important que cette thèse sous-tend est que l'enseignement efficace n'est pas seulement constitué de techniques et de méthodologie, mais plutôt d'attitude et d'approche envers l'enseignement. Ceci ne veut pas nécessairement dire que plusieurs méthodes d'enseignement reçues dans un cours avec l'intention d'optimaliser les mécanismes de transmission et d'assimilation de la matière sont inappropriées. Cependant, l'absence de ce que nous pourrions définir comme un ton pédagogique est essentiel, c'est-à-dire, qu'une attitude positive à la productivité autant vis-à-vis de la matière à transmettre que vis-à-vis de l'individu impliqué dans "l'acte" de réception versus la découverte, aura davantage de succès. Toute autre méthode sera complètement inefficace, inaccessible, voire même inutile. D'emblée, dans l'hypothèse de départ, l'argument principal présente une attitude générale d'enseignement à divers échelons ; soit au niveau secondaire ou collégial qui est inappropriée, incomplète ou négative. En d'autres mots, cette approche thérapeutise l'éducation. Dans l'exercice de cette approche, l'enseignant ou l'enseignante adopte plutôt le rôle d'un thérapeute que celui d'un éducateur. De ce fait, le professeur en situation a une approche plutôt de thérapeute que celle de maître-précepteur et que la matière présentée est souvent diluée, et réduite à des niveaux d'apprentissage accompagnés de carences notoires et d'échecs académiques. Les attentes d'une performance dans le milieu académique sont souvent des plus modestes. Cette même tendance d'une éducation à la baisse est évidente aussi dans le processus d'évaluation. Il est certain que dans les disciplines non scientifiques, l'évaluation formative a grandement suivi l'évaluation normative conduisant le précepteur, tour à tour, dans une évaluation dormative dans laquelle l'effort et l'intention remplacent les aptitudes et les habilitées réelles. Si l'approche pédagogique est vraiment l'élément crucial de l'éducation, il Importe que l'approche générale influence le climat de l'éducation contemporaine, de fait, devienne un palliatif contre-productif souvent réhabilitant. De plus, cette pseudo-thérapie d'où d'écoule une attitude exigeante envers l'enseignant et l'apprenant dont le fondement est la reconnaissance des impératifs culturels qui en sont le reflet et le corps doit-être affirmé et transposé dans la réalité. Cette dernière comprend des attentes très poussées en ce qui concerne la performance en classe et aussi le respect de la matière qui contient la présentation routinière et fondamentale; renouveau intense du processus d'évaluation qui fournira des standards communs et des objectifs externes dans l'évaluation du travail de l'étudiant. Cette connaissance et domestication empirique que nous présente Vygotsky dans un climat contemporain qu'il a expliqué ces termes comme "des zones de développement proximales" basées sur la doctrine suivante que le bon apprentissage précède le développement et que conséquemment s'ensuit une pédagogie d'apprentissage plutôt qu'une pédagogie centrée sur l'apprenant. L'application significative de ces derniers principes ou de ces épistémologiques s'imbriquent dans une situation d'apprentissage ascentionnel dont la structure est détaillée et considérée par différentes perspectives de la recherche qui suit.||Abstract : The central tenet of this thesis is that effective teaching is not only and perhaps not primarily a matter of technique and methodology but of attitude and approach. This is not to say that diverse methods of classroom instruction intended to optimize the mechanics of transmission and the assimilation of data are inappropriate but that in the absence of what we might denominate as a certain pedagogical tone. that is, a productive attitude toward both the material to be conveyed and the individuel engaged in the 'act' of reception-and-discovery, even the most powerful methods will be differentially unavailing or, at best, inefficient. Given this initial assumption, the argument proceeds that the general attitude toward instruction currently in place at the secondary echelons, that is, on the high school and college levels, may be popularly represented as a 'teaching down' approach, in other words, as one which seeks to therapeuticize education. In practice this means that the teacher tends to manifest in situ more as a therapist than as a preceptor, that the material to be presented is frequently diluted or scaled down to perceived levels of cognitive (dis)ability (as is also the case with the rate of instruction), and that performance expectations in the current pedagogical milieu are commonly quite modest. The same downward trend is evident in assessment protocols as well. Certainly in the nonscientific disciplines, normative evaluation has been widely succeeded by formative evaluation, leading in turn to a peculiar kind of dormative evaluation in which intangibles such as effort and intention may deputize for realized ability. If pedagogical approach is indeed the crucial element in instruction, and if the general approach that pervades the contemporary climate of instruction is indeed counter-productively remedial or rehabilitory, that is, therapeutic, then it should follow that a more demanding attitude toward teaching and learning founded on the recognition of the culturel imperative which teaching both reflects and embodies needs to be re-affirmed and translated into practice. This latter would entail the maintenance of high expectations with regard to classroom performance, a respect for the material which precludes its routine mitigation or debasement, a renewed insistance on grading protocols that provide an external, 'objective' or communal standard against which the student's work can be measured, the empirical acknowledgment or domestication of what Vygotsky has termed "the zone of proximal development," based on the doctrine that good learning proceeds in advance of development, and conséquently, a learning-centered rather than learner-centered pedagogy. The meaningful application of this latter set of principles or epistemological gradients comprises the 'learning up' situation whose structure is excunined in some détail and considered from various perspectives in the ensuing.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Investigating problem-based learning in Saudi Arabian mathematics education: a TIMSS-related study

The aim of this study is to investigate the effectiveness of problem-based learning (PBL) on students’ mathematical performance. This includes mathematics achievement and students’ attitudes towards mathematics for third and eighth grade students in Saudi Arabia. Mathematics achievement includes, knowing, applying, and reasoning domains, while students’ attitudes towards mathematics covers, ‘Like learning mathematics’, ‘value mathematics’, and ‘a confidence to learn mathematics’. This study goes deeper to examine the interaction of a PBL teaching strategy, with trained face-to-face and self-directed learning teachers, on students’ performance (mathematics achievement and attitudes towards mathematics). It also examines the interaction between different ability levels of students (high and low levels) with a PBL teaching strategy (with trained face-to-face or self-directed learning teachers) on students’ performance. It draws upon findings and techniques of the TIMSS international benchmarking studies. Mixed methods are used to analyse the quasi-experimental study data. One -way ANOVA, Mixed ANOVA, and paired t-tests models are used to analyse quantitative data, while a semi-structured interview with teachers, and author’s observations are used to enrich understanding of PBL and mathematical performance. The findings show that the PBL teaching strategy significantly improves students’ knowledge application, and is better than the traditional teaching methods among third grade students. This improvement, however, occurred only with the trained face-to-face teacher’s group. Furthermore, there is robust evidence that using a PBL teaching strategy could raise significantly students’ liking of learning mathematics, and confidence to learn mathematics, more than traditional teaching methods among third grade students. Howe ver, there was no evidence that PBL could improve students’ performance (mathematics achievement and attitudes towards mathematics), more than traditional teaching methods, among eighth grade students. In 8th grade, the findings for low achieving students show significant improvement compared to high achieving students, whether PBL is applied or not. However, for 3th grade students, no significant difference in mathematical achievement between high and low achieving students was found. The results were not expected for high achieving students and this is also discussed. The implications of these findings for mathematics education in Saudi Arabia are considered.