Metacognitive Strategies and Learning

During the past decade, metacognition has been identified not only as a component of cognition but also as an important factor in learning. This practitioner proposes that educators and educational researchers should focus on the development and implementation of metacognitive learning strategies. The existing metacognitive studies have concentrated on several areas. One area centers on the continuing efforts to identify all the elements of metacognition. Another area concentrates on the roles that metacognition plays in specific learning behaviors that occur at various ages and levels of complexity. The third area investigates the relationships of metacognition to specific content areas of learning by focusing on the effects of metacognitive learning strategies. The most common areas of study have been reading comprehension, math skills, writing skills, and applying metacognitive strategies to learn various subjects using the computer. Directly or indirectly, the existing studies relate to the expanding applications of the relationships and relevancies of metacognition to learning. Considerable evidence confirms that when students use metacognitive strategies they often experience a higher level of learning. This practitioner believes that experiencing higher levels of learning gives students the confidence they need to construct knowledge which promotes lifelong learning.

Effects of Cooperative and Individual Integrated Learning System on Attitudes and Achievement in Mathematics

The purpose of this study was to determine the effects of a computer-based Integrated Learning Systems (ILS) model used with adult high school students engaging mathematics activities. This study examined achievement, attitudinal and behavior differences between students completing ILS activities in a traditional, individualized format compared to cooperative learning groups.

Classroom Simulation is Computer Based

The authors review and evaluate the use of a business simulation, specifically he Hotel Operational Training Simulation (HOTS), in the fourth year of a hospitality undergraduate program. Four dimensions were explored: learning experience, alternative method of instruction, critical and analytical thinking ability and delivery time frame, in addition to the student overall satisfaction with the learning experience.

Achievement of generic baccalaureate student nurses using computer -assisted instruction simulations during a nursing review course

The use of computer assisted instruction (CAI) simulations as an instructional strategy provides nursing students with a critical thinking approach for evaluating risks and benefits and choosing correct alternatives in "safe" patient care situations. It was hypothesized that using CAI simulations during an upper level nursing review course would have a positive effect on the students' posttest scores. Subjects (n = 36) were senior nursing students enrolled in a nursing review course in an undergraduate baccalaureate program. A limitation of the study was the small sample size. The study employed a modified group experimental design using the t test for independent samples. The group who received the CAI simulations during the physiological system review demonstrated a significant increase (p $<$.01) in the posttest score mean when compared to the lecture-discussion group score mean. There was no significant difference between high and low clinical grade point average (GPA) students in the CAI and lecture-discussion groups and their score means on the posttest. However, score mean differences of the low clinical GPA students showed a greater increase for the CAI group than the lecture-discussion group. There was no significant difference between the groups in their system content subscore means on the exit examination completed three weeks later. It was concluded that CAI simulations are as effective as lecture-discussion in assisting upper level students to process information for clinical decision making. CAI simulations can be considered as an instructional strategy to supplement or replace lecture content during a review course, allowing more efficient use of faculty time. It is recommended that the study be repeated using a larger sample size. Further investigations are recommended in comparing the effectiveness of computer software formats and various instructional strategies for other learning situations and student populations. ^

An investigation of computer anxiety among vocational -technical teachers in Dade County, Florida

The purpose of this research was to investigate the relationship of computer anxiety to selected demographic variables: learning styles, age, gender, ethnicity, teaching/professional areas, educational level, and school types among vocational-technical educators.^ The subjects (n = 202) were randomly selected vocational-technical educators from Dade County Public School System, Florida, stratified across teaching/professional areas. All subjects received the same survey package in the spring of 1996. Subjects self-reported their learning style and level of computer anxiety by completing Kolb's Learning Style Inventory (LSI) and Oetting's Computer Anxiety Scale (COMPAS, Short Form). Subjects' general demographic information and their experience with computers were collected through a self-reported Participant Inventory Form.^ The distribution of scores suggested that some educators (25%) experienced some overall computer anxiety. There were significant correlations between computer related experience as indicated by self-ranked computer competence and computer based training and computer anxiety. One-way analyses of variance (ANOVA) indicated no significant differences between computer anxiety and/or computer related experiences, and learning style, age, and ethnicity. There were significant differences between educational level, teaching area, school type, and computer anxiety and/or computer related experiences. T-tests indicated significant differences between gender and computer related experiences. However, there was no difference between gender and computer anxiety.^ Analyses of covariance (ANCOVA) were performed for each independent variable on computer anxiety, with computer related experiences (self-ranked computer competence and computer based training) as the respective covariates. There were significant main effects for the educational level and school type on computer anxiety. All other variables were insignificant on computer anxiety. ANCOVA also revealed an effect for learning style varied notably on computer anxiety. All analyses were conducted at the.05 level of significance. ^

The efficacy of an interactive computer system for teaching developmental mathematics to college students

Many students are entering colleges and universities in the United States underprepared in mathematics. National statistics indicate that only approximately one-third of students in developmental mathematics courses pass. When underprepared students repeatedly enroll in courses that do not count toward their degree, it costs them money and delays graduation. This study investigated a possible solution to this problem: Whether using a particular computer assisted learning strategy combined with using mastery learning techniques improved the overall performance of students in a developmental mathematics course. Participants received one of three teaching strategies: (a) group A was taught using traditional instruction with mastery learning supplemented with computer assisted instruction, (b) group B was taught using traditional instruction supplemented with computer assisted instruction in the absence of mastery learning and, (c) group C was taught using traditional instruction without mastery learning or computer assisted instruction. Participants were students in MAT1033, a developmental mathematics course at a large public 4-year college. An analysis of covariance using participants' pretest scores as the covariate tested the null hypothesis that there was no significant difference in the adjusted mean final examination scores among the three groups. Group A participants had significantly higher adjusted mean posttest score than did group C participants. A chi-square test tested the null hypothesis that there were no significant differences in the proportions of students who passed MAT1033 among the treatment groups. It was found that there was a significant difference in the proportion of students who passed among all three groups, with those in group A having the highest pass rate and those in group C the lowest. A discriminant factor analysis revealed that time on task correctly predicted the passing status of 89% of the participants. ^ It was concluded that the most efficacious strategy for teaching developmental mathematics was through the use of mastery learning supplemented by computer-assisted instruction. In addition, it was noted that time on task was a strong predictor of academic success over and above the predictive ability of a measure of previous knowledge of mathematics.^

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) interven- tions 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 relationship between student characteristics, computer literacy,technology acceptance, and distance education student satisfaction

The purpose of this study is to identify the relationship between the characteristics of distance education students, their computer literacy and technology acceptance and distance education course satisfaction. The theoretical framework for this study will apply Rogers and Havelock's Innovation, Diffusion & Utilization theories to distance education. It is hypothesized that technology acceptance and computer competency will influence the student course satisfaction and explain the decision to adopt or reject distance education curriculum and technology. Distance education delivery, Institutional Support, Convenience, Interactivity and five distance education technologies were studied. The data were collected by a survey questionnaire sent to four Florida universities. Three hundred and nineteen and students returned the questionnaire. A factor and regression analysis on three measure of satisfaction revealed significant difference between the three main factors related to the overall satisfaction of distance education students and their adoption of distance education technology as medium of learning. Computer literacy is significantly related to greater overall student satisfaction. However, when competing with other factors such as delivery, support, interactivity, and convenience, computer literacy is not significant. Results indicate that age and status are the only two student characteristics to be significant. Distance education technology acceptance is positively related to higher overall satisfaction. Innovativeness is also positively related to student overall satisfaction. Finally, the technology used relates positively to greater satisfaction levels within the educational experience. Additional research questions were investigated and provided insights into the innovation decision process.

User Interface Design for Electronic Learning Software: Promoting Usability and Facilitating Learning for Children

Computing devices have become ubiquitous in our technologically-advanced world, serving as vehicles for software applications that provide users with a wide array of functions. Among these applications are electronic learning software, which are increasingly being used to educate and evaluate individuals ranging from grade school students to career professionals. This study will evaluate the design and implementation of user interfaces in these pieces of software. Specifically, it will explore how these interfaces can be developed to facilitate the use of electronic learning software by children. In order to do this, research will be performed in the area of human-computer interaction, focusing on cognitive psychology, user interface design, and software development. This information will be analyzed in order to design a user interface that provides an optimal user experience for children. This group will test said interface, as well as existing applications, in order to measure its usability. The objective of this study is to design a user interface that makes electronic learning software more usable for children, facilitating their learning process and increasing their academic performance. This study will be conducted by using the Adobe Creative Suite to design the user interface and an Integrated Development Environment to implement functionality. These are digital tools that are available on computing devices such as desktop computers, laptops, and smartphones, which will be used for the development of software. By using these tools, I hope to create a user interface for electronic learning software that promotes usability while maintaining functionality. This study will address the increasing complexity of computing software seen today – an issue that has risen due to the progressive implementation of new functionality. This issue is having a detrimental effect on the usability of electronic learning software, increasing the learning curve for targeted users such as children. As we make electronic learning software an integral part of educational programs in our schools, it is important to address this in order to guarantee them a successful learning experience.

User Interface Design for Electronic Learning Software: Promoting Usability and Facilitating Learning for Children

Computing devices have become ubiquitous in our technologically-advanced world, serving as vehicles for software applications that provide users with a wide array of functions. Among these applications are electronic learning software, which are increasingly being used to educate and evaluate individuals ranging from grade school students to career professionals. This study will evaluate the design and implementation of user interfaces in these pieces of software. Specifically, it will explore how these interfaces can be developed to facilitate the use of electronic learning software by children. In order to do this, research will be performed in the area of human-computer interaction, focusing on cognitive psychology, user interface design, and software development. This information will be analyzed in order to design a user interface that provides an optimal user experience for children. This group will test said interface, as well as existing applications, in order to measure its usability. The objective of this study is to design a user interface that makes electronic learning software more usable for children, facilitating their learning process and increasing their academic performance. This study will be conducted by using the Adobe Creative Suite to design the user interface and an Integrated Development Environment to implement functionality. These are digital tools that are available on computing devices such as desktop computers, laptops, and smartphones, which will be used for the development of software. By using these tools, I hope to create a user interface for electronic learning software that promotes usability while maintaining functionality. This study will address the increasing complexity of computing software seen today – an issue that has risen due to the progressive implementation of new functionality. This issue is having a detrimental effect on the usability of electronic learning software, increasing the learning curve for targeted users such as children. As we make electronic learning software an integral part of educational programs in our schools, it is important to address this in order to guarantee them a successful learning experience.

A deep learning-based approach for 3D people tracking

Questa tesi si occupa dell’estensione di un framework software finalizzato all'individuazione e al tracciamento di persone in una scena ripresa da telecamera stereoscopica. In primo luogo è rimossa la necessità di una calibrazione manuale offline del sistema sfruttando algoritmi che consentono di individuare, a partire da un fotogramma acquisito dalla camera, il piano su cui i soggetti tracciati si muovono. Inoltre, è introdotto un modulo software basato su deep learning con lo scopo di migliorare la precisione del tracciamento. Questo componente, che è in grado di individuare le teste presenti in un fotogramma, consente ridurre i dati analizzati al solo intorno della posizione effettiva di una persona, escludendo oggetti che l’algoritmo di tracciamento sarebbe portato a individuare come persone.

Learning Meta-Descriptions of the FOAF Network

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An Analysis of Individual Differences for a Computer Self-Regulated Reading Comprehension Task

Recent developments in brain imagery have made it possible to explore links between brain functions and psychological phenomena, opening a window between mind, brain and behavior. However, behavior cannot be understood solely by looking at the brain alone; the roles of the context, task, and practice are potent forces in shaping behavior. According to these ideas, we present a work experience to reflect on: 1) the variations of how people learn, 2) the learning potential of students with learning disabilities, and 3) computers as a tool to learn and to analyze student’s reading comprehension processes. In this vein, we present and discuss an example of how different types of readers (average, dyslexia, and hemispherectomy) undertake a computer self-regulated reading comprehension task. This is not an experimental research study and results cannot be generalized. Theoretical and educational implications are discussed in line with the proposed aims.

Learning from Geometry

Subspaces and manifolds are two powerful models for high dimensional signals. Subspaces model linear correlation and are a good fit to signals generated by physical systems, such as frontal images of human faces and multiple sources impinging at an antenna array. Manifolds model sources that are not linearly correlated, but where signals are determined by a small number of parameters. Examples are images of human faces under different poses or expressions, and handwritten digits with varying styles. However, there will always be some degree of model mismatch between the subspace or manifold model and the true statistics of the source. This dissertation exploits subspace and manifold models as prior information in various signal processing and machine learning tasks.

A near-low-rank Gaussian mixture model measures proximity to a union of linear or affine subspaces. This simple model can effectively capture the signal distribution when each class is near a subspace. This dissertation studies how the pairwise geometry between these subspaces affects classification performance. When model mismatch is vanishingly small, the probability of misclassification is determined by the product of the sines of the principal angles between subspaces. When the model mismatch is more significant, the probability of misclassification is determined by the sum of the squares of the sines of the principal angles. Reliability of classification is derived in terms of the distribution of signal energy across principal vectors. Larger principal angles lead to smaller classification error, motivating a linear transform that optimizes principal angles. This linear transformation, termed TRAIT, also preserves some specific features in each class, being complementary to a recently developed Low Rank Transform (LRT). Moreover, when the model mismatch is more significant, TRAIT shows superior performance compared to LRT.

The manifold model enforces a constraint on the freedom of data variation. Learning features that are robust to data variation is very important, especially when the size of the training set is small. A learning machine with large numbers of parameters, e.g., deep neural network, can well describe a very complicated data distribution. However, it is also more likely to be sensitive to small perturbations of the data, and to suffer from suffer from degraded performance when generalizing to unseen (test) data.

From the perspective of complexity of function classes, such a learning machine has a huge capacity (complexity), which tends to overfit. The manifold model provides us with a way of regularizing the learning machine, so as to reduce the generalization error, therefore mitigate overfiting. Two different overfiting-preventing approaches are proposed, one from the perspective of data variation, the other from capacity/complexity control. In the first approach, the learning machine is encouraged to make decisions that vary smoothly for data points in local neighborhoods on the manifold. In the second approach, a graph adjacency matrix is derived for the manifold, and the learned features are encouraged to be aligned with the principal components of this adjacency matrix. Experimental results on benchmark datasets are demonstrated, showing an obvious advantage of the proposed approaches when the training set is small.

Stochastic optimization makes it possible to track a slowly varying subspace underlying streaming data. By approximating local neighborhoods using affine subspaces, a slowly varying manifold can be efficiently tracked as well, even with corrupted and noisy data. The more the local neighborhoods, the better the approximation, but the higher the computational complexity. A multiscale approximation scheme is proposed, where the local approximating subspaces are organized in a tree structure. Splitting and merging of the tree nodes then allows efficient control of the number of neighbourhoods. Deviation (of each datum) from the learned model is estimated, yielding a series of statistics for anomaly detection. This framework extends the classical {\em changepoint detection} technique, which only works for one dimensional signals. Simulations and experiments highlight the robustness and efficacy of the proposed approach in detecting an abrupt change in an otherwise slowly varying low-dimensional manifold.

Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.