The effects of using an alternative method for teaching badminton to post secondary level students in Taiwan

The effect of teaching method in physical education is an important issue and has been a concern of the expert teacher. Teachers are expected to create a model of teaching in their field; therefore, it is reasonable to question what is the effect of an alternative teaching method on student performance in physical education. This study explores whether teaching methods with advanced planning, behavior and belief in high enthusiasm, use of instructional strategies and evaluation, together termed a systematic teaching approach, used in a physical education activity would provide an effective environment for learning which supports student achievement in the psychomotor, cognitive, and affective domains. This study also investigated whether there was a difference in performance between students who were taught with a systematic teaching approach and students who were taught with the traditional teaching model. Information was collected using two performance skills, a written test, and one questionnaire. The 68 participants were randomly assigned into either an experimental group or a control group. Two teachers were assigned to either the experimental group or the control group. The teaching experiment took place at Tamsui Oxford University College in Taiwan and lasted eight weeks. ^ Research questions were analyzed using the t-test. Results indicated that a significant difference in students' performance was found between the experimental group and the control group on both the skill tests and the paper test. Analysis of student attitude toward their teacher and their course on the questionnaire indicated a significant difference between the experimental group and the control group. ^ The findings of this study imply that students who were taught with a systematic teaching style were significantly superior to students who were taught with the traditional model on these measures. This finding supports the contention that effective teaching in physical education is related to advanced planning, high enthusiasm, instructional strategy and evaluation and that all physical education teachers should implement these planning elements in the development of the teaching strategies. ^

Enhanced anchorage of tissue-engineered cartilage using an osteoinductive approach

Articular cartilage injuries occur frequently in the knee joint. Several methods have been implemented clinically, to treat osteochondral defects but none have been able to produce a long term, durable solution. Photopolymerizable cartilage tissue engineering approaches appear promising; however, fundamentally, forming a stable interface between the tissue engineered cartilage and native tissue, mainly subchondral bone and native cartilage, remains a major challenge. The overall objective of this research is to find a solution for the current problem of dislodgment of tissue engineered cartilage at the defect site for the treatment of degraded cartilage that has been caused due to knee injuries or because of mild to moderate level of osteoarthritis. For this, an in-vitro model was created to analyze the integration of tissue engineered cartilage with the bone, healthy and diseased cartilage over time. We investigated the utility of hydroxyapatite (HA) nanoparticles to promote controlled bone-growth across the bone-cartilage interface in an in vitro engineered tissue model system using bone marrow derived stem cells. We also investigated the application of HA nanoparticles to promote enhance integration between tissue engineered cartilage and native cartilage both in healthy and diseased states. Samples incorporated with HA demonstrated significantly higher interfacial shear strength (at the junction between engineered cartilage and engineered bone and also with diseased cartilage) compared to the constructs without HA (p < 0.05), after 28 days of culture. These findings indicate that the incorporation of HA nanoparticles permits more stable anchorage of the injectable hydrogel-based engineered cartilage construct via augmented integration between bone and cartilage.^

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].

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].^

Development of a new student evaluation instrument of instructor effectiveness in online courses

The purpose of this study was to (a) develop an evaluation instrument capable of rating students' perceptions of the instructional quality of an online course and the instructor’s performance, and (b) validate the proposed instrument with a study conducted at a major public university. The instrument was based upon the Seven Principles of Good Practice for Undergraduate Education (Chickering & Gamson, 1987). The study examined four specific questions. 1. Is the underlying factor structure of the new instrument consistent with Chickering and Gamson's Seven Principles? 2. Is the factor structure of the new instrument invariant for male and female students? 3. Are the scores on the new instrument related students’ expected grades? 4. Are the scores on the new instrument related to the students' perceived course workload? ^ The instrument was designed to measure students’ levels of satisfaction with their instruction, and also gathered information concerning the students’ sex, the expected grade in the course, and the students’ perceptions of the amount of work required by the course. A cluster sample consisting of an array of online courses across the disciplines yielded a total 297 students who responded to the online survey. The students for each course selected were asked to rate their instructors with the newly developed instrument. ^ Question 1 was answered using exploratory factor analysis, and yielded a factor structure similar to the Seven Principles.^ Question 2 was answered by separately factor-analyzing the responses of male and female students and comparing the factor structures. The resulting factor structures for men and women were different. However, 14 items could be realigned under five factors that paralleled some of the Seven Principles. When the scores of only those 14 items were entered in two principal components factor analyses using only men and only women, respectively and restricting the factor structure to five factors, the factor structures were the same for men and women.^ A weak positive relationship between students’ expected grades and their scores on the instrument was found (Question 3). There was no relationship between students’ perceived workloads for the course and their scores on the instrument (Question 4).^

Targeted Brain Derived Neurotropic Factors (BDNF) Delivery across the Blood-Brain Barrier for Neuro-Protection Using Magnetic Nano Carriers: An In-Vitro Study

Parenteral use of drugs; such as opiates exert immunomodulatory effects and serve as a cofactor in the progression of HIV-1 infection, thereby potentiating HIV related neurotoxicity ultimately leading to progression of NeuroAIDS. Morphine exposure is known to induce apoptosis, down regulate cAMP response element-binding (CREB) expression and decrease in dendritic branching and spine density in cultured cells. Use of neuroprotective agent; brain derived neurotropic factor (BDNF), which protects neurons against these effects, could be of therapeutic benefit in the treatment of opiate addiction. Previous studies have shown that BDNF was not transported through the blood brain barrier (BBB) in-vivo.; and hence it is not effectivein-vivo. Therefore development of a drug delivery system that can cross BBB may have significant therapeutic advantage. In the present study, we hypothesized that magnetically guided nanocarrier may provide a viable approach for targeting BDNF across the BBB. We developed a magnetic nanoparticle (MNP) based carrier bound to BDNF and evaluated its efficacy and ability to transmigrate across the BBB using an in-vitro BBB model. The end point determinations of BDNF that crossed BBB were apoptosis, CREB expression and dendritic spine density measurement. We found that transmigrated BDNF was effective in suppressing the morphine induced apoptosis, inducing CREB expression and restoring the spine density. Our results suggest that the developed nanocarrier will provide a potential therapeutic approach to treat opiate addiction, protect neurotoxicity and synaptic density degeneration.