How Students Study? An Analysis of Learning Strategies in Engineering and Technology Courses

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

The effect of cooperative learning on the attitudes toward science and the achievement of students in a non-science majors' general biology laboratory course at an urban community college

The purpose of this study was to evaluate the effect of cooperative learning strategies on students' attitudes toward science and achievement in BSC 1005L, a non-science majors' general biology laboratory course at an urban community college. Data were gathered on the participants' attitudes toward science and cognitive biology level pre and post treatment in BSC 1005L. Elements of the Learning Together model developed by Johnson and Johnson and the Student Team-Achievement Divisions model created by Slavin were incorporated into the experimental sections of BSC 1005L.^ Four sections of BSC 1005L participated in this study. Participants were enrolled in the 1998 spring (January) term. Students met weekly in a two hour laboratory session. The treatment was administered to the experimental group over a ten week period. A quasi-experimental pretest-posttest control group design was used. Students in the cooperative learning group (n$\sb1$ = 27) were administered the Test of Science-Related Attitudes (TOSRA) and the cognitive biology test at the same time as the control group (n$\sb2$ = 19) (at the beginning and end of the term).^ Statistical analyses confirmed that both groups were equivalent regarding ethnicity, gender, college grade point average and number of absences. Independent sample t-tests performed on pretest mean scores indicated no significant differences in the TOSRA scale two or biology knowledge between the cooperative learning group and the control group. The scores of TOSRA scales: one, three, four, five, six, and seven were significantly lower in the cooperative learning group. Independent sample t-tests of the mean score differences did not show any significant differences in posttest attitudes toward science or biology knowledge between the two groups. Paired t-tests did not indicate any significant differences on the TOSRA or biology knowledge within the cooperative learning group. Paired t-tests did show significant differences within the control group on TOSRA scale two and biology knowledge. ANCOVAs did not indicate any significant differences on the post mean scores of the TOSRA or biology knowledge adjusted by differences in the pretest mean scores. Analysis of the research data did not show any significant correlation between attitudes toward science and biology knowledge. ^

Group presence in virtual worlds: Supporting collaborative e-learning

Even though e-learning endeavors have significantly proliferated in recent years, current e-learning technologies provide poor support for group-oriented learning. The now popular virtual world's technologies offer a possible solution. Virtual worlds provide the users with a 3D - computer generated shared space in which they can meet and interact through their virtual representations. Virtual worlds are very successful in developing high levels of engagement, presence and group presence in the users. These elements are also desired in educational settings since they are expected to enhance performance. The goal of this research is to test the hypothesis that a virtual world learning environment provides better support for group-oriented collaborative e-learning than other learning environments, because it facilitates the emergence of group presence. To achieve this, a quasi-experimental study was conducted and data was gathered through the use of various survey instruments and a set of collaborative tasks assigned to the participants. Data was gathered on the dependent variables: Engagement, Group Presence, Individual Presence, Perceived Individual Presence, Perceived Group Presence and Performance. The data was analyzed using the statistical procedures of Factor Analysis, Path Analysis, Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA). The study provides support for the hypothesis. The results also show that virtual world learning environments are better than other learning environments in supporting the development of all the dependent variables. It also shows that while only Individual Presence has a significant direct effect on Performance; it is highly correlated with both Engagement and Group Presence. This suggests that these are also important in regards to performance. Developers of e-learning endeavors and educators should incorporate virtual world technologies in their efforts in order to take advantage of the benefit they provide for e-learning group collaboration.

Statistical dialog management for health interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. ^ Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. ^ The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. ^ In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.^

Learning and Exploiting Camera Geometry for Computer Vision

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

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

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

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

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

Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

Statistical Dialog Management for Health Interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.

Digital anthropology and educational eGames: learning through behavioural patterns in digital, game-based contexts

The selected publications are focused on the relations between users, eGames and the educational context, and how they interact together, so that both learning and user performance are improved through feedback provision. A key part of this analysis is the identification of behavioural, anthropological patterns, so that users can be clustered based on their actions, and the steps taken in the system (e.g. social network, online community, or virtual campus). In doing so, we can analyse large data sets of information made by a broad user sample,which will provide more accurate statistical reports and readings. Furthermore, this research is focused on how users can be clustered based on individual and group behaviour, so that a personalized support through feedback is provided, and the personal learning process is improved as well as the group interaction. We take inputs from every person and from the group they belong to, cluster the contributions, find behavioural patterns and provide personalized feedback to the individual and the group, based on personal and group findings. And we do all this in the context of educational games integrated in learning communities and learning management systems. To carry out this research we design a set of research questions along the 10-year published work presented in this thesis. We ask if the users can be clustered together based on the inputs provided by them and their groups; if and how these data are useful to improve the learner performance and the group interaction; if and how feedback becomes a useful tool for such pedagogical goal; if and how eGames become a powerful context to deploy the pedagogical methodology and the various research methods and activities that make use of that feedback to encourage learning and interaction; if and how a game design and a learning design must be defined and implemented to achieve these objectives, and to facilitate the productive authoring and integration of eGames in pedagogical contexts and frameworks. We conclude that educational games are a resourceful tool to provide a user experience towards a better personalized learning performance and an enhance group interaction along the way. To do so, eGames, while integrated in an educational context, must follow a specific set of user and technical requirements, so that the playful context supports the pedagogical model underneath. We also conclude that, while playing, users can be clustered based on their personal behaviour and interaction with others, thanks to the pattern identification. Based on this information, a set of recommendations are provided Digital Anthropology and educational eGames 6 /216 to the user and the group in the form of personalized feedback, timely managed for an optimum impact on learning performance and group interaction level. In this research, Digital Anthropology is introduced as a concept at a late stage to provide a backbone across various academic fields including: Social Science, Cognitive Science, Behavioural Science, Educational games and, of course, Technology-enhance learning. Although just recently described as an evolution of traditional anthropology, this approach to digital behaviour and social structure facilitates the understanding amongst fields and a comprehensive view towards a combined approach. This research takes forward the already existing work and published research onusers and eGames for learning, and turns the focus onto the next step — the clustering of users based on their behaviour and offering proper, personalized feedback to the user based on that clustering, rather than just on isolated inputs from every user. Indeed, this pattern recognition in the described context of eGames in educational contexts, and towards the presented aim of personalized counselling to the user and the group through feedback, is something that has not been accomplished before.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

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.

Machine learning and data decompositions for complex networked dynamical systems

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

Essays on Machine Learning and Hedonic Models

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

Predominant learning styles among pharmacy students at the Federal University of Paraná, Brazil

Background: Learning styles are cognitive, emotional, and physiological traits, as well as indicators of how learners perceive, interact, and respond to their learning environments. According to Honey-Mumford, learning styles are classified as active, reflexive, theoretical, and pragmatic. Objective: The purpose of this study was to identify the predominant learning styles among pharmacy students at the Federal University of Paraná, Brazil. Methods: An observational, cross-sectional, and descriptive study was conducted using the Honey-Alonso Learning Style Questionnaire. Students in the Bachelor of Pharmacy program were invited to participate in this study. The questionnaire comprised 80 randomized questions, 20 for each of the four learning styles. The maximum possible score was 20 points for each learning style, and cumulative scores indicated the predominant learning styles among the participants. Honey-Mumford (1986) proposed five preference levels for each style (very low, low, moderate, high, and very high), called a general interpretation scale, to avoid student identification with one learning style and ignoring the characteristics of the other styles. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 20.0. Results: This study included 297 students (70% of all pharmacy students at the time) with a median age of 21 years old. Women comprised 77.1% of participants. The predominant style among pharmacy students at the Federal University of Paraná was the pragmatist, with a median of 14 (high preference). The pragmatist style prevails in people who are able to discover techniques related to their daily learning because such people are curious to discover new strategies and attempt to verify whether the strategies are efficient and valid. Because these people are direct and objective in their actions, pragmatists prefer to focus on practical issues that are validated and on problem situations. There was no statistically significant difference between genders with regard to learning styles. Conclusion: The pragmatist style is the prevailing style among pharmacy students at the Federal University of Paraná. Although students may have a learning preference that preference is not the only manner in which students can learn, neither their preference is the only manner in which students can be taught. Awareness of students’ learning styles can be used to adapt the methodology used by teachers to render the teaching-learning process effective and long lasting. The content taught to students should be presented in different manners because varying teaching methods can develop learning skills in students.

Movement Sonification: Effects on Motor Learning beyond Rhythmic Adjustments

Motor learning is based on motor perception and emergent perceptual-motor representations. A lot of behavioral research is related to single perceptual modalities but during last two decades the contribution of multimodal perception on motor behavior was discovered more and more. A growing number of studies indicates an enhanced impact of multimodal stimuli on motor perception, motor control and motor learning in terms of better precision and higher reliability of the related actions. Behavioral research is supported by neurophysiological data, revealing that multisensory integration supports motor control and learning. But the overwhelming part of both research lines is dedicated to basic research. Besides research in the domains of music, dance and motor rehabilitation, there is almost no evidence for enhanced effectiveness of multisensory information on learning of gross motor skills. To reduce this gap, movement sonification is used here in applied research on motor learning in sports. Based on the current knowledge on the multimodal organization of the perceptual system, we generate additional real-time movement information being suitable for integration with perceptual feedback streams of visual and proprioceptive modality. With ongoing training, synchronously processed auditory information should be initially integrated into the emerging internal models, enhancing the efficacy of motor learning. This is achieved by a direct mapping of kinematic and dynamic motion parameters to electronic sounds, resulting in continuous auditory and convergent audiovisual or audio-proprioceptive stimulus arrays. In sharp contrast to other approaches using acoustic information as error-feedback in motor learning settings, we try to generate additional movement information suitable for acceleration and enhancement of adequate sensorimotor representations and processible below the level of consciousness. In the experimental setting, participants were asked to learn a closed motor skill (technique acquisition of indoor rowing). One group was treated with visual information and two groups with audiovisual information (sonification vs. natural sounds). For all three groups learning became evident and remained stable. Participants treated with additional movement sonification showed better performance compared to both other groups. Results indicate that movement sonification enhances motor learning of a complex gross motor skill-even exceeding usually expected acoustic rhythmic effects on motor learning.

The Effectiveness of Outdoor Education on Environmental Learning, Appreciation, and Activism

The main objective of this research was to determine the effectiveness of outdoor education on student knowledge retention, appreciation for nature, and environmental activism in a college level course on south Florida ecology. Six class sections were given quizzes on four course topics either post-lecture or post-field trip. Students were also given pre-course and post-course opinion surveys. Although mean quiz scores for the post-field trip were higher than for the post-lecture, statistical analysis determined that there was no significant difference in quiz scores for location taken (post-lecture or post-field trip). Survey results show a correlation between knowledge of environmental issues and environmental activism. Even though student survey responses point to outdoor education and field trips being the most effective method of learning and influential on appreciation for nature, the quiz scores do not reflect such.