Predicting Student Performance and Differences in Learning Styles based on Textual Complexity Indices applied on Blog and Microblog Posts

Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In contrast, the approach proposed in this paper relies on the actual content analysis of each student's contributions in a learning environment. More specifically, in this study, textual complexity analysis is applied to investigate how student's writing style on social media tools can be used to predict their academic performance and their learning style. Multiple textual complexity indices are used for analyzing the blog and microblog posts of 27 students engaged in a project-based learning activity. The preliminary results of this pilot study are encouraging, with several indexes predictive of student grades and/or learning styles.

Professional development of teacher-educators towards transformative learning

This study explores the specific characteristics of teacher-educator professional development interventions that enhance their transformative learning towards stimulating the inquiry-based attitude of students. An educational design research method was followed. Firstly, in partnership with five experienced educators, a professional development programme was designed, tested and redesigned. Secondly, a qualitative multiple case study was conducted to examine the active ingredients of the designed interventions with regard to educators changes in beliefs and behaviour. The study was carried out in four different educational settings in which 20 educators participated during nine months. Data sources included videos, questionnaires, interviews and written personal theories of practice. The analyses indicated that aligned self-study interventions on a personal, peer and group level guided by a trained facilitator supported the intended leaning.

Is Our Students Learning - QuestionMark: Extending E-Assessment Systems for Adapative Marking and Feedback

Our key contribution is a flexible, automated marking system that adds desirable functionality to existing E-Assessment systems. In our approach, any given E-Assessment system is relegated to a data-collection mechanism, whereas marking and the generation and distribution of personalised per-student feedback is handled separately by our own system. This allows content-rich Microsoft Word feedback documents to be generated and distributed to every student simultaneously according to a per-assessment schedule.

The feedback is adaptive in that it corresponds to the answers given by the student and provides guidance on where they may have gone wrong. It is not limited to simple multiple choice which are the most prescriptive question type offered by most E-Assessment Systems and as such most straightforward to mark consistently and provide individual per-alternative feedback strings. It is also better equipped to handle the use of mathematical symbols and images within the feedback documents which is more flexible than existing E-Assessment systems, which can only handle simple text strings.

As well as MCQs the system reliably and robustly handles Multiple Response, Text Matching and Numeric style questions in a more flexible manner than Questionmark: Perception and other E-Assessment Systems. It can also reliably handle multi-part questions where the response to an earlier question influences the answer to a later one and can adjust both scoring and feedback appropriately.

New question formats can be added at any time provided a corresponding marking method conforming to certain templates can also be programmed. Indeed, any question type for which a programmatic method of marking can be devised may be supported by our system. Furthermore, since the student’s response to each is question is marked programmatically, our system can be set to allow for minor deviations from the correct answer, and if appropriate award partial marks.

Virtual Reality as a tool to Assist Learning in Aerospace Assembly

This study examines whether virtual reality (VR) is more superior to paper-based instructions in increasing the speed at which individuals learn a new assembly task. Specifically, the work seeks to quantify any learning benefits when individuals have been given the opportunity and compares the performance of two groups using virtual and hardcopy media types to pre-learn the task. A build experiment based on multiple builds of an aircraft panel showed that a group of people who pre-learned the assembly task using a VR environment completed their builds faster (average build time 29.5% lower). The VR group also made fewer references to instructional materials (average number of references 38% lower) and made fewer errors than a group using more traditional, hard copy instructions. These outcomes were more pronounced during build one with differences in build time and number of references showing limited statistical differences.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

E-Government Collaboration in the Swedish Public Sector : Multiple Studies on Collaboration Facilitators and Collaboration Modes

Collaboration in the public sector is imperative to achieve e-government objectives such as improved efficiency and effectiveness of public administration and improved quality of public services. Collaboration across organizational and institutional boundaries requires public organizations to share e-government systems and services through for instance, interoperable information technology and processes. Demands on public organizations to become more open also require that public organizations adopt new collaborative approaches for inviting and engaging citizens in governmental activities. E-government related collaboration in the public sector is challenging, however, and collaboration initiatives often fail. Public organizations need to learn how to collaborate since forms of e-government collaboration and expected outcomes are mostly unknown. How public organizations can collaborate and the expected outcomes are thus investigated in this thesis by studying multiple collaboration cases on the acquisition and implementation of a particular e-government investment (digital archive). This thesis also investigates how e-government collaboration can be facilitated through artifacts. It is done through a case study, where objects that cross boundaries between collaborating communities in the public sector are studied, and by designing a configurable process model integrating several processes for social services. By using design science, this thesis also investigates how an m-government solution that facilitates collaboration between citizens and public organizations can be designed. The thesis contributes to literature through describing five different modes of interorganizational collaboration in the public sector and the expected benefits from each mode. It also contributes with an instantiation of a configurable process model supporting three open social e-services and with evidence of how it can facilitate collaboration. This thesis further describes how boundary objects facilitate collaboration between different communities in an open government design initiative. It contributes with a designed mobile government solution, thereby providing proof of concept and initial design implications for enabling collaboration with citizens through citizen sourcing (outsourcing a governmental activity to citizens through an open call). This thesis also identifies research streams within e-government collaboration research through a literature review and the thesis contributions are related to the identified research streams. This thesis gives directions for future research by suggesting that future research should focus further on understanding e-government collaboration and how information and communication technology can facilitate collaboration in the public sector. It is suggested that further research should investigate m-government solutions to form design theories. Future research should also examine how value can be co-created in e-government collaboration.

Evaluating statistical and machine learning methods to predict risk of in-hospital child mortality in Uganda

Thesis (Master's)--University of Washington, 2016-08

Affect-Based Effects of Simulation Games on Learning

Empirical evidence has demonstrated the benefits of using simulation games in enhancing learning especially in terms of cognitive gains. This is to be expected as the dynamism and non-linearity of simulation games are more cognitively demanding. However, the other effects of simulation games, specifically in terms of learners’ emotions, have not been given much attention and are under-investigated. This study aims to demonstrate that simulation games stimulate positive emotions from learners that help to enhance learning. The study finds that the affect-based constructs of interest, engagement and appreciation are positively correlated to learning. A stepwise multiple regression analysis shows that a model involving interest and engagement are significantly associated with learning. The emotions of learners should be considered in the development of curriculum, and the delivery of learning and teaching as positive emotions enhances learning.

Understanding Teacher and Student Learning Situated in a School-wide Implementation of Fractions Instruction

Thesis (Ph.D.)--University of Washington, 2016-06

Children with Learning Disabilities and Challenging Behaviour: Relationship between Parents and Professionals

This multi-perspectival Interpretive Phenomenological Analysis (IPA) study explored how people in the ‘networks of concern’ talked about how they tried to make sense of the challenging behaviours of four children with severe learning disabilities. The study also aimed to explore what affected relationships between people. The study focussed on 4 children through interviewing their mothers, their teachers and the Camhs Learning Disability team members who were working with them. Two fathers also joined part of the interviews. All interviews were conducted separately using a semi-structured approach. IPA allowed both a consideration of the participant’s lived experiences and ‘objects of concern’ and a deconstruction of the multiple contexts of people’s lives, with a particular focus on disability. The analysis rendered five themes: the importance of love and affection, the difficulties, and the differences of living with a challenging child, the importance of being able to make sense of the challenges and the value of good relationships between people. Findings were interpreted through the lens of CMM (Coordinated Management of Meaning), which facilitated a systemic deconstruction and reconstruction of the findings. The research found that making sense of the challenges was a key concern for parents. Sharing meanings were important for people’s relationships with each other, including employing diagnostic and behavioural narratives. The importance of context is also highlighted including a consideration of how societal views of disability have an influence on people in the ‘network of concern’ around the child. A range of systemic approaches, methods and techniques are suggested as one way of improving services to these children and their families. It is suggested that adopting a ‘both/and’ position is important in such work - both applying evidence based approaches and being alert to and exploring the different ways people try and make sense of the children’s challenges. Implications for practice included helping professionals be alert to their constructions and professional narratives, slowing the pace with families, staying close to the concerns of families and addressing network issues.

Music Learning Networks: Supporting the Music Learning of Adolescents

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

Conditions for Teaching and Learning in Religious Education (RE) : Perspectives of Teachers and Pupils at the Beginning of the 6th Grade in Sweden

In this article empirical findings from interviews with teachers of three classes of 12-year-old pupils are presented, together with questionnaire-responses from these 54 pupils. The interviews focus on teaching aims for Religious Education (RE), a subject that in Sweden, besides dealing with religion, also explores other kinds of beliefs, ethics and life questions. In the questionnaire the pupils are asked to solve four RE tasks with content that is central from a Swedish curriculum perspective. The research involves pupils at the beginning of the sixth grade and the purpose of this article is to look at the teachers’ aims and the pupils’ responses, and consider what these may indicate about conditions for teaching and learning RE in these classes. The findings show that the perspectives of the pupils at the beginning of the sixth grade seem to be rather far from the expectations of the RE syllabus. The pupils’ statements are rather vague with regard to religion as a phenomenon and there are few examples of pupils interpreting religious symbols in a way that is useful in further analysis. While existential and ethical plots, messages and point of views are comparatively easy to describe, it is harder to express multiple perspectives, reasons, comparisons and questions. A problem for the teachers in developing the perspectives of their pupils is that they find it hard to say what kind of general difficulties pupils have in RE, a fact that makes it hard to direct the teaching. Another challenge is that the teachers’ RE-aims are rather overarching and primarily related to fostering fundamental values. What improves the conditions for teaching and learning is the teachers’ concern for the pupils and their relationships with the teacher and with each other, a factor which is of vital importance for learning and which can also be used as a specific teaching method in subject matter education.

'Stepping out' with Gargantua : learning new research practices in the educational theatre of absurdity

Access to new forms, conduct and practices of educational research remain elusive providing researchers stay within the narrow theoretical constructs-the static, single vista ofconventional research models. This dissertation presents the findings of an experimental study that aims to extend the discourse of educational research through a 'performative ethnographic analysis' by using a single-site case study approach. The case study is an analytical parody based on multiple discourse relevant to a 'new' and different approach to educational research so that a more comprehensive and complex process of reading and writing text becomes possible. Throughout this process, a generative methodology and interpretative base are anticipated to provide a metaphoric focus for a critical dialogue. The discourse informing the theoretical and interpretative base of the study include philosophy, science, visual arts, literary theory, critical postructuralist theory and theatre performance. The data are presented as a series of performance narratives in the form of socio-drama, interspersed with critical reflection that enables the researcher, the research participant and reader to become part ofa triadic construct. The findings from this study have major implications for informing contemporary educational research, as they demonstrate that by approaching research in 'new' and different ways, the researcher and the educational community have access to insights that are unavailable within the constraints of conventional models ofresearch.

'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.