The Planned and the Emergent: An Alternative Model of Learning and Literacy

Within academic institutions, writing centers are uniquely situated, socially rich sites for exploring learning and literacy. I examine the work of the Michigan Tech Writing Center's UN 1002 World Cultures study teams primarily because student participants and Writing Center coaches are actively engaged in structuring their own learning and meaning-making processes. My research reveals that learning is closely linked to identity formation and leading the teams is an important component of the coaches' educational experiences. I argue that supporting this type of learning requires an expanded understanding of literacy and significant changes to how learning environments are conceptualized and developed. This ethnographic study draws on data collected from recordings and observations of one semester of team sessions, my own experiences as a team coach and UN 1002 teaching assistant, and interviews with Center coaches prior to their graduation. I argue that traditional forms of assessment and analysis emerging from individualized instruction models of learning cannot fully account for the dense configurations of social interactions identified in the Center's program. Instead, I view the Center as an open system and employ social theories of learning and literacy to uncover how the negotiation of meaning in one context influences and is influenced by structures and interactions within as well as beyond its boundaries. I focus on the program design, its enaction in practice, and how engagement in this type of writing center work influences coaches' learning trajectories. I conclude that, viewed as participation in a community of practice, the learning theory informing the program design supports identity formation —a key aspect of learning as argued by Etienne Wenger (1998). The findings of this study challenge misconceptions of peer learning both in writing centers and higher education that relegate peer tutoring to the role of support for individualized models of learning. Instead, this dissertation calls for consideration of new designs that incorporate peer learning as an integral component. Designing learning contexts that cultivate and support the formation of new identities is complex, involves a flexible and opportunistic design structure, and requires the availability of multiple forms of participation and connections across contexts.

The Impact of a Classroom Performance System on Learning Gains in a Biology Course for Science Majors

This study was conducted to determine if the use of the technology known as Classroom Performance System (CPS), specifically referred to as “Clickers”, improves the learning gains of students enrolled in a biology course for science majors. CPS is one of a group of developing technologies adapted for providing feedback in the classroom using a learner-centered approach. It supports and facilitates discussion among students and between them and teachers, and provides for participation by passive students. Advocates, influenced by constructivist theories, claim increased academic achievement. In science teaching, the results have been mixed, but there is some evidence of improvements in conceptual understanding. The study employed a pretest-posttest, non-equivalent groups experimental design. The sample consisted of 226 participants in six sections of a college biology course at a large community college in South Florida with two instructors trained in the use of clickers. Each instructor randomly selected their sections into CPS (treatment) and non-CPS (control) groups. All participants filled out a survey that included demographic data at the beginning of the semester. The treatment group used clicker questions throughout, with discussions as necessary, whereas the control groups answered the same questions as quizzes, similarly engaging in discussion where necessary. The learning gains were assessed on a pre/post-test basis. The average learning gains, defined as the actual gain divided by the possible gain, were slightly better in the treatment group than in the control group, but the difference was statistically non-significant. An Analysis of Covariance (ANCOVA) statistic with pretest scores as the covariate was conducted to test for significant differences between the treatment and control groups on the posttest. A second ANCOVA was used to determine the significance of differences between the treatment and control groups on the posttest scores, after controlling for sex, GPA, academic status, experience with clickers, and instructional style. The results indicated a small increase in learning gains but these were not statistically significant. The data did not support an increase in learning based on the use of the CPS technology. This study adds to the body of research that questions whether CPS technology merits classroom adaptation.

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^

The effect of mobile technology as an active student response system on the acquisition of U.S. history content of secondary students with specific learning disabilities

Students with specific learning disabilities (SLD) typically learn less history content than their peers without disabilities and show fewer learning gains. Even when they are provided with the same instructional strategies, many students with SLD struggle to grasp complex historical concepts and content area vocabulary. Many strategies involving technology have been used in the past to enhance learning for students with SLD in history classrooms. However, very few studies have explored the effectiveness of emerging mobile technology in K-12 history classrooms. ^ This study investigated the effects of mobile devices (iPads) as an active student response (ASR) system on the acquisition of U.S. history content of middle school students with SLD. An alternating treatments single subject design was used to compare the effects of two interventions. There were two conditions and a series of pretest probesin this study. The conditions were: (a) direct instruction and studying from handwritten notes using the interactive notebook strategy and (b) direct instruction and studying using the Quizlet App on the iPad. There were three dependent variables in this study: (a) percent correct on tests, (b) rate of correct responses per minute, and (c) rate of errors per minute. ^ A comparative analysis suggested that both interventions (studying from interactive notes and studying using Quizlet on the iPad) had varying degrees of effectiveness in increasing the learning gains of students with SLD. In most cases, both interventions were equally effective. During both interventions, all of the participants increased their percentage correct and increased their rate of correct responses. Most of the participants decreased their rate of errors. ^ The results of this study suggest that teachers of students with SLD should consider a post lesson review in the form of mobile devices as an ASR system or studying from handwritten notes paired with existing evidence-based practices to facilitate students’ knowledge in U.S. history. Future research should focus on the use of other interactive applications on various mobile operating platforms, on other social studies subjects, and should explore various testing formats such as oral question-answer and multiple choice. ^

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

From the workplace to the classroom:examining the impact of self-leadership learning strategies on higher educational attainment and success

Self-leadership is a concept from the organisational and management literature broadly combining processes of self-goal setting, self-regulation and self-motivation. Research has typically focused on the impact of self-leadership on work performance outcomes, with little attention to potential benefits for learning and development. In this paper, we employ a longitudinal design to examine the association of a number of processes of self-leadership with higher educational attainment in a sample of business students (N = 150). Self-reported use of strategies related to behavioural, cognitive and motivational aspects of self-leadership were measured in the first semester of the academic year, and correlated with end-of year grade point average. We found that in particular, self-goal setting, pro-active goal-related behaviour, behaviour regulation and direction, motivational awareness, and optimism were all significant predictors of educational attainment. We discuss implications for educational research and for teachers and tutors in practice.

Learning curves: analysing pace and challenge in four successful puzzle games

The pace at which challenges are introduced in a game has long been identified as a key determinant of both the enjoyment and difficulty experienced by game players, and their ability to learn from game play. In order to understand how to best pace challenges in games, there is great value in analysing games already demonstrated as highly engaging. Play-through videos of four puzzle games (Portal, Portal 2 Co-operative mode, Braid and Lemmings), were observed and analysed using metrics derived from a behavioural psychology understanding of how people solve problems. Findings suggest that; 1) the main skills learned in each game are introduced separately, 2) through simple puzzles that require only basic performance of that skill, 3) the player has the opportunity to practice and integrate that skill with previously learned skills, and 4) puzzles increase in complexity until the next new skill is introduced. These data provide practical guidance for designers, support contemporary thinking on the design of learning structures in games, and suggest future directions for empirical research.

An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.

The Proces of Curriculum and Module Design

The outcome of the inductive decision -making process of the leading project management group (PMG) was the proposal to develop three modules, Human Resource Management and Knowledge Management, Quality Management and Intercultural management, each for 10 ECTS credits. As a result of the theoretical and organisational framework and analytical phase of the project, four strategies informed the development and implemen- tation of the modules: 1. Collaboration as a principle stemming from EU collaborative policy and receiving it’s expression on all implementation levels (designing the modules, modes of learning, delivering the modules, evaluation process). 2. Building on the Bologna process masters level framework to assure ap- propriate academic level of outputs. 3. Development of value -based leadership of students through transforma- tional learning in a cross -cultural setting and continual reflection of theory in practice. 4. Continual evaluation and feedback among teachers and students as a strategy to achieve a high quality programme. In the first phase of designing the modules the collaborative strategy in particular was applied, as each module was led by one university, but members from all other universities participated in the discussions and development of the mod- ules. The Bologna process masters level framework and related standards and guidelines informed the form and method of designing the modules.

Teaching and learning sexuality in nursing education

Sexuality is recognized as part of holistic nursing care, but its inclusion in clinical practice and nursing training is inconsistent. Based on the question "How students and teachers acknowledge sexuality in teaching and learning?", we developed a study in order to characterize the process of teaching and learning sexuality in a micro perspective of curriculum development. We used a mixed methods design with a sequential strategy: QUAN-qual of descriptive and explanatory type. 646 students and teachers participated. The quantitative component used questionnaire surveys. Document analysis was used in the additional component. A curricular dimension of sexuality emerges guided by a behaviourist line and based on a biological vision. The issues considered sage are highlighted and framed in steps of adolescence and adulthood and more attacghed to female sexuality and procreative aspect. There is in emeergence a hidden curriculum by reference to content from other dimensions of sexuality but less often expressed. Theoretical learning follows a communicational model of reality through abstraction strategies, which infers a deductive method of learning, with a behaviourist approach to assessment. Clinical teaching adresses sexuality in combination with reproductive lealth nursing. The influencing factors of teaching and learning of sexuality were also explored. We conclude that the vision of female sexuality taught and learned in relation to women has a projection of care in clinical practice based on the same principles

A study of the role of a technology-enhanced learning implementation group in mediating an institutional VLE minimum standards policy

Recent years have seen a focus on responding to student expectations in higher education. As a result, a number of technology-enhanced learning (TEL) policies have stipulated a requirement for a minimum virtual learning environment (VLE) standard to provide a consistent student experience. This paper offers insight into an under-researched area of such a VLE standard policy development using a case study of one university. With reference to the implementation staircase model, this study takes cue from the view that an institutional VLE template can affect lower levels directly, sidestepping the chain in the implementation staircase. The Group's activity whose remit is to design and develop a VLE template, therefore, becomes significant. The study, drawing on activity theory, explores the mediating role of such a Group. Factors of success and sources of tension are analysed to understand the interaction between the individuals and the collective agency of Group members. The paper identifies implications to practice for similar TEL development projects. Success factors identified demonstrated the importance of good project management principles, establishing clear rules and division of labour for TEL development groups. One key finding is that Group members are needed to draw on both different and shared mediating artefacts, supporting the conclusion that the nature of the group's composition and the situated expertise of its members are crucial for project success. The paper's theoretical contribution is an enhanced representation of a TEL policy implementation staircase.

A review of predictive factors of student success in and satisfaction with online learning

Students perceive online courses differently than traditional courses. Negative perceptions can lead to unfavourable learning outcomes including decreased motivation and persistence. Throughout this review, a broad range of factors that affect performance and satisfaction within the online learning environment for adult learners will be examined including learning outcomes, instructional design and learner characteristics, followed by suggestions for further research, and concluding with implications for online learning pertinent to administrators, instructors, course designers and students. Online learning may not be appropriate for every student. Identifying particular characteristics that contribute to online success versus failure may aid in predicting possible learning outcomes and save students from enrolling in online courses if this type of learning environment is not appropriate for them. Furthermore, knowing these learner attributes may assist faculty in designing quality online courses to meet students’ needs. Adequate instructional methods, support, course structure and design can facilitate student performance and satisfaction.

Continual learning for computer vision applications

One of the most visionary goals of Artificial Intelligence is to create a system able to mimic and eventually surpass the intelligence observed in biological systems including, ambitiously, the one observed in humans. The main distinctive strength of humans is their ability to build a deep understanding of the world by learning continuously and drawing from their experiences. This ability, which is found in various degrees in all intelligent biological beings, allows them to adapt and properly react to changes by incrementally expanding and refining their knowledge. Arguably, achieving this ability is one of the main goals of Artificial Intelligence and a cornerstone towards the creation of intelligent artificial agents. Modern Deep Learning approaches allowed researchers and industries to achieve great advancements towards the resolution of many long-standing problems in areas like Computer Vision and Natural Language Processing. However, while this current age of renewed interest in AI allowed for the creation of extremely useful applications, a concerningly limited effort is being directed towards the design of systems able to learn continuously. The biggest problem that hinders an AI system from learning incrementally is the catastrophic forgetting phenomenon. This phenomenon, which was discovered in the 90s, naturally occurs in Deep Learning architectures where classic learning paradigms are applied when learning incrementally from a stream of experiences. This dissertation revolves around the Continual Learning field, a sub-field of Machine Learning research that has recently made a comeback following the renewed interest in Deep Learning approaches. This work will focus on a comprehensive view of continual learning by considering algorithmic, benchmarking, and applicative aspects of this field. This dissertation will also touch on community aspects such as the design and creation of research tools aimed at supporting Continual Learning research, and the theoretical and practical aspects concerning public competitions in this field.

The three-dimensional single-bin-size bin packing problem: combining metaheuristic and machine learning approaches

The Three-Dimensional Single-Bin-Size Bin Packing Problem is one of the most studied problem in the Cutting & Packing category. From a strictly mathematical point of view, it consists of packing a finite set of strongly heterogeneous “small” boxes, called items, into a finite set of identical “large” rectangles, called bins, minimizing the unused volume and requiring that the items are packed without overlapping. The great interest is mainly due to the number of real-world applications in which it arises, such as pallet and container loading, cutting objects out of a piece of material and packaging design. Depending on these real-world applications, more objective functions and more practical constraints could be needed. After a brief discussion about the real-world applications of the problem and a exhaustive literature review, the design of a two-stage algorithm to solve the aforementioned problem is presented. The algorithm must be able to provide the spatial coordinates of the placed boxes vertices and also the optimal boxes input sequence, while guaranteeing geometric, stability, fragility constraints and a reduced computational time. Due to NP-hard complexity of this type of combinatorial problems, a fusion of metaheuristic and machine learning techniques is adopted. In particular, a hybrid genetic algorithm coupled with a feedforward neural network is used. In the first stage, a rich dataset is created starting from a set of real input instances provided by an industrial company and the feedforward neural network is trained on it. After its training, given a new input instance, the hybrid genetic algorithm is able to run using the neural network output as input parameter vector, providing as output the optimal solution. The effectiveness of the proposed works is confirmed via several experimental tests.