Committing Curricular Time to Science Literacy: The Benefits from Science-based Media Resources

Science reported in the media is an authentic source material to explore science research and innovation, to learn how science works and to consolidate science literacy skills.

Media reports intended to communicate science research and innovation provide opportunities for teachers to develop among their pupils the critical reading skills that are essential for promoting literacy in science.

This study focuses on a curricular intervention with upper primary pupils (age 11 years) and uses science reported in the media to facilitate science directed learning in the primary curriculum.

The study suggests that the use of science based media reports can be a positive learning experience for pupils. Strategies and teaching approaches can be used to boost pupils’ confidence and competence to adopt critical reading strategies when they encounter science-based media.

Critical reading and reasoning strategies vary in their degree of difficulty. This study would suggest that, when using media-based resources, teachers need approaches that systematically address the different levels of cognative challenge presented by media resources and create opportunities within the curriculum to revisit, consolidate and develop pupils’ critical reasoning skills.

Efficient Learning of Bayesian Networks with Bounded Tree-width

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods \cite{korhonen2exact, nie2014advances} tackle the problem by using $k$-trees to learn the optimal Bayesian network with tree-width up to $k$. Finding the best $k$-tree, however, is computationally intractable. In this paper, we propose a sampling method to efficiently find representative $k$-trees by introducing an informative score function to characterize the quality of a $k$-tree. To further improve the quality of the $k$-trees, we propose a probabilistic hill climbing approach that locally refines the sampled $k$-trees. The proposed algorithm can efficiently learn a quality Bayesian network with tree-width at most $k$. Experimental results demonstrate that our approach is more computationally efficient than the exact methods with comparable accuracy, and outperforms most existing approximate methods.

A Biocultural Examination of Student Learning Behaviors in Large Undergraduate Lectures

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

Designing and Developing Digital and Blended Learning Solutions

The continuous advancement in computing, together with the decline in its cost, has resulted in technology becoming ubiquitous (Arbaugh, 2008, Gros, 2007). Technology is growing and is part of our lives in almost every respect, including the way we learn. Technology helps to collapse time and space in learning. For example, technology allows learners to engage with their instructors synchronously, in real time and also asynchronously, by enabling sessions to be recorded. Space and distance is no longer an issue provided there is adequate bandwidth, which determines the most appropriate format such text, audio or video. Technology has revolutionised the way learners learn; courses are designed; and ‘lessons’ are delivered, and continues to do so. The learning process can be made vastly more efficient as learners have knowledge at their fingertips, and unfamiliar concepts can be easily searched and an explanation found in seconds. Technology has also enabled learning to be more flexible, as learners can learn anywhere; at any time; and using different formats, e.g. text or audio. From the perspective of the instructors and L&D providers, technology offers these same advantages, plus easy scalability. Administratively, preparatory work can be undertaken more quickly even whilst student numbers grow. Learners from far and new locations can be easily accommodated. In addition, many technologies can be easily scaled to accommodate new functionality and/ or other new technologies. ‘Designing and Developing Digital and Blended Learning Solutions’ (5DBS), has been developed to recognise the growing importance of technology in L&D. This unit contains four learning outcomes and two assessment criteria, which is the same for all other units, besides Learning Outcome 3 which has three assessment criteria. The four learning outcomes in this unit are: • Learning Outcome 1: Understand current digital technologies and their contribution to learning and development solutions; • Learning Outcome 2: Be able to design blended learning solutions that make appropriate use of new technologies alongside more traditional approaches; • Learning Outcome 3: Know about the processes involved in designing and developing digital learning content efficiently and what makes for engaging and effective digital learning content; • Learning Outcome 4: Understand the issues involved in the successful implementation of digital and blended learning solutions. Each learning outcome is an individual chapter and each assessment unit is allocated its own sections within the respective chapters. This first chapter addresses the first learning outcome, which has two assessment criteria: summarise the range of currently available learning technologies; critically assess a learning requirement to determine the contribution that could be made through the use of learning technologies. The introduction to chapter one is in Section 1.0. Chapter 2 discusses the design of blended learning solutions in consideration of how digital learning technologies may support face-to-face and online delivery. Three learning theory sets: behaviourism; cognitivism; constructivism, are introduced, and the implication of each set of theory on instructional design for blended learning discussed. Chapter 3 centres on how relevant digital learning content may be created. This chapter includes a review of the key roles, tools and processes that are involved in developing digital learning content. Finally, Chapter 4 concerns delivery and implementation of digital and blended learning solutions. This chapter surveys the key formats and models used to inform the configuration of virtual learning environment software platforms. In addition, various software technologies which may be important in creating a VLE ecosystem that helps to enhance the learning experience, are outlined. We introduce the notion of personal learning environment (PLE), which has emerged from the democratisation of learning. We also review the roles, tools, standards and processes that L&D practitioners need to consider within a delivery and implementation of digital and blended learning solution.

Transformational Leadership and Job-related Learning

Various empirical studies have examined transformational leadership on the effects of followers and organisations. Transformational leadership has been related to individual attitudes and behaviors such as satisfaction with leaders, organisational citizenship behavior, organisational commitment, motivation, trust in leader, creativity, performance, employee involvement, and empowerment. It has also been linked to such organisational outcomes as innovation, change, productivity, ethical climate, and organisational learning. Organisational learning occurs at three levels: individual, group, and organisational. The focus of the present study is on the individual level—job-related learning. Job-related learning is a measure of individual job behavior pertaining to acquisition of knowledge and skills and enhancement of job performance within the context of the individual’s workplace. It argues that transformational leadership inculcates individuals’ drive to learn. The aim of the study is to examine the relationship between transformational leadership and job-related learning. Transformational leadership is composed of four unique but interrelated facets--idealised influence, individualised consideration, inspirational motivation, and intellectual stimulation. The research results support the hypothesis that transformational leadership is positively related to job-related learning. The implications of the research findings and suggestions for future research are discussed.

Training Finnish and English Speakers to Produce Non-native Vowels

The subject of this thesis was the acquisition of difficult non-native vowels by speakers of two different languages. In order to study the subject, a group of Finnish speakers and another group of American English speakers were recruited and they underwent a short listen-and-repeat training that included as stimuli the semisynthetically created pseudowords /ty:ti/ and /tʉ:ti/. The aim was to study the effect of the training method on the subjects as well as the possible influence of the speakers’ native language on the process of acquisition. The selection of the target vowels /y/ and /ʉ/ was made according to the Speech Learning Model and Perceptual Assimilation Model, both of which predict that second language speech sounds that share similar features with sounds of a person’s native language are most difficult for the person to learn. The vowel /ʉ/ is similar to Finnish vowels as well as to vowels of English, whereas /y/ exists in Finnish but not in English, although it is similar to other English vowels. Therefore, it can be hypothesized that /ʉ/ is a difficult vowel for both groups to learn and /y/ is difficult for English speakers. The effect of training was tested with a pretest-training-posttest protocol in which the stimuli were played alternately and the subjects’ task was to repeat the heard stimuli. The training method was thought to improve the production of non-native sounds by engaging different feedback mechanisms, such as auditory and somatosensory. These, according to Template Theory, modify the production of speech by altering the motor commands from the internal speech system or the feedforward signal which translates the motoric commands into articulatory movements. The subjects’ productions during the test phases were recorded and an acoustic analysis was performed in which the formant values of the target vowels were extracted. Statistical analyses showed a statistically significant difference between groups in the first formant, signaling a possible effect of native motor commands. Furthermore, a statistically significant difference between groups was observed in the standard deviation of the formants in the production of /y/, showing the uniformity of native production. The training had no observable effect, possibly due to the short nature of the training protocol.

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

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

Gender Differences in Early Reading Strategies: a Comparison of Synthetic Phonics Only with a Mixed Approach to Teaching Reading to 4-5 Year-Old Children

A survey of primary schools in England found that girls outperform boys in English across all phases (Ofsted in Moving English forward. Ofsted, Manchester, 2012). The gender gap remains an on-going issue in England, especially for reading attainment. This paper presents evidence of gender differences in learning to read that emerged during the development of a reading scheme for 4- and 5-year-old children in which 372 children from Reception classes in sixteen schools participated in 12-month trials. There were three arms per trial: Intervention non-PD (non-phonically decodable text with mixed methods teaching); Intervention PD (phonically decodable text with mixed methods teaching); and a ‘business as usual’ control condition SP (synthetic phonics and decodable text). Assignment to Intervention condition was randomised. Standardised measures of word reading and comprehension were used. The research provides statistically significant evidence suggesting that boys learn more easily using a mix of whole-word and synthetic phonics approaches. In addition, the evidence indicates that boys learn to read more easily using the natural-style language of ‘real’ books including vocabulary which goes beyond their assumed decoding ability. At post-test, boys using the nonphonically decodable text with mixed methods (Intervention A) were 8 months ahead in reading comprehension compared to boys using a wholly synthetic phonics approach.

Closing the Gap: Young People's Views on Learning at School

The UK has been criticised for its inequitable education system, as student outcomes are strongly linked to parental socio-economic status. Children and young people experiencing poverty are less likely than their better off peers to leave school with good grades, which can perpetuate disadvantage in later life. The attainment gap between children and young people experiencing poverty and their better off peers in the UK is widening, despite an increasing media and policy focus in this area. Poverty-related educational inequality is a complex area and there is no conclusive evidence in what works to reduce its effects. While there is a plethora of research on the impact of poverty on education, very little of it includes the voice of children and young people and/or the psychological impact of poverty on learning. The importance of hearing the views of children and young people is central to educational psychology, as is social justice and facilitating access to the curriculum for all students. The barriers presented by the experience of poverty to learning are thus vital for educational psychologists to address. This study used qualitative methods to explore the learning journey of Key Stage 3 (age 12-13) young people experiencing poverty in an English coastal borough. Questions from the Little Box of Big Questions 2 were used as a tool in semi-structured interviews, in addition to questions devised by the researcher. Young people discussed aspects of their lives that enabled them to learn at school, and aspects that presented barriers to learning. The research used Positive Psychology, taking a strengths based approach to explore the skills young people thought they brought to education, skills they would like to develop, and how they could be supported in this. The study has highlighted themes that, if addressed, could potentially raise the attainment of children and young people experiencing poverty.

Techniques for batch reinforcement learning in robotics

This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.

BET on Top Hat: challenges to improve language proficiency

Within the business context, communication and interaction tends to be considerably rooted in the use of English (as lingua franca), as well as in ICT use. Thus, professionals have to be able to speak the English language, resorting to specific, internationally recognised terminology and be proficient in the use of manifold ICT tools. In fact, the tendency is for the great majority of higher education (HE) students to own mobile devices (laptops, smartphones and/or tablets) and use them to access information and communicate/interact with content and other people. Bearing this in mind, a teaching and learning strategy was designed, in which m-learning (i.e. learning in which the delivery platform is a mobile device) was used to approach Business English Terminology (BET). The strategy was labelled as ‘BET on Top Hat’, once the selected application was Top Hat (https://tophat.com/) and the idea was for students to face it as if it were a game/challenge. In this scenario, the main goals of this exploratory study were to find evidence as to: i) the utility of m-learning activities for learning BET and ii) if and how m-learning activities can generate intrinsic motivation in students to learn BET. Participants (n=23) were enrolled in English II, a curricular unit of the 1st cycle degree in Retail Management offered at Águeda School of Technology and Management – University of Aveiro (2014/15 edition). The data gathered included the students’ results in quizzes and their answers to a short final evaluation questionnaire regarding their experience with BET on Top Hat. Consequently, data were treated and analysed resorting to descriptive statistical analysis, and, when considered pertinent, the teacher’s observation notes were also considered. The results unveil that, on the one hand, the strategy had a clear positive impact on the students’ intrinsic motivation and, on the other hand, the students’ performance as to BET use tended to improve over time.

School-Based Referrals to the Juvenile Courts: Prevalence and Characteristics in Several States

Effective school discipline practices are essential to keeping schools safe and creating an optimal learning environment. However, the overreliance of exclusionary discipline often removes students from the school setting and deprives them of the opportunity to learn. Previous research has suggested that students are being introduced to the juvenile justice system through the use of school-based juvenile court referrals. In 2011, approximately 1.2 million delinquency cases were referred to the juvenile courts in the United States. Preliminary evidence suggests that an increasing number of these referrals have originated in the schools. This study investigated school-based referrals to the juvenile courts as an element of the School-to-Prison Pipeline (StPP). The likelihood of school-based juvenile court referrals and rate of dismissal of these referrals was examined in several states using data from the National Juvenile Court Data Archives. In addition, the study examined race and special education status as predictors of school-based juvenile court referrals. Descriptive statistics, logistic regression and odds ratio, were used to analyze the data, make conclusions based on the findings and recommend appropriate school discipline practices.

Finnish Preclinical and Clinical Medical Students as Strategic L2 English Learners: Language Learning Strategies and Communicating with Patients

This thesis focused on medical students’ language learning strategies for patient encounters. The research questions concerned the types of learning strategies that medical students use and the differences between the preclinical students and the clinical students, two groups who have had varying amounts of experience with patients. Additionally, strategy use was examined through activity systems to gain information on the context of language learning strategy use in order to learn language for patient encounters. In total, 130 first-year medical students (preclinical) and 39 fifth-year medical students (clinical) participated in the study by filling in a questionnaire on language learning strategies. In addition, two students were interviewed in order to create activity systems for the medical students at different stages of their studies. The study utilised both quantitative and qualitative research methods; the analysis of the results relies on Oxford’s Strategic Self-Regulation Model in the quantitative part and on activity theory in the qualitative part. The theoretical sections of the study introduced earlier research and theories regarding English for specific purposes, language learning strategies and activity theory. The results indicated that the medical students use affective, sociocultural-interactive and metasociocultural-interactive strategies often and avoid using negative strategies, which hinder language learning or cease communication altogether. Slight differences between the preclinical and clinical students were found, as clinical students appear to use affective and metasociocultural-interactive strategies more frequently compared to the preclinical students. The activity systems of the two students interviewed were rather similar. The students were at different stages of their studies, but their opinions were very similar. Both reported the object of learning to be mutual understanding between the patient and the doctor, which in part explains the preference for strategies that support communication and interaction. The results indicate that the nature of patient encounters affects the strategy use of the medical students at least to some extent.

Education at last! Taiwanese grandmothers "go to school"

Language socialization is a life-long process in which individuals are continuously socialized into new roles, statuses, and practices. This process becomes more complex in multilingual contexts. However, we know little of the language socialization of older adults and we know even less of minority-speaking elders' experiences of linguistic marginalization in contemporary communicative milieus. In this ethnographic and discourse-analytic study, I examine the language socialization of non-Mandarin-speaking elderly Taiwanese women in senior adult education programs in a rural township in Taiwan. Through examining autobiographical narratives, master narratives about elders, and classroom discourses, this study explores the historical construction of their sociolinguistic marginalization and their negotiation and resistance of such marginalization. The majority of the elderly women were denied education when they were young. Some received Japanese education during the Japanese colonization period. While the uneducated and illiterate elders have a strong aspiration for learning, they are dismissed as "unable to learn" by their teachers, peer students, and community leaders. By contrast, the Japanese literate exhibit a strong learning identity associated with colonial modernity. These two groups, however, have to contend with the social stereotype associated with their non-Mandarin speaking status. Under a Mandarin-only ideology that links Mandarin with modernity, discourses that have actively mobilized the category of “illiterate” to reference the older population are part and parcel of Taiwan’s modern identity. By demonstrating how these women are treated, in official discourses and in classroom interactions, as children for their lack of Mandarin abilities, I argue that the literacy education that set out to “compensate” these women for their earlier lack of educational opportunity has paradoxically reinforced their marginalization. Further, in recent years, they have become even more marginalized as the government has prioritized the education of recent young female marriage immigrants from Southeast Asia, who are considered in charge of educating the “future sons and daughters of Taiwan.” This research demonstrates how language socialization is a contested and life-long process and calls attention to the effects of language ideologies on literacy and language education. The findings have policy implications for improving literacy and language education both within Taiwan and elsewhere in the world.

'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.