Studies have shown an association between argumentative discourse in science class, better understanding of science concepts, and improved academic performance. However, there is lack of research on how argumentation can increase student motivation. This mixed methods concurrent nested study uses Bandura's construct of motivation and concepts of argumentation and formative feedback to understand how teachers orchestrate argumentation in science class and how it affects motivation. Qualitative data was collected through interviews of 4 grade-9 science teachers and through observing teacher-directed classroom discourse. Classroom observations allowed the researcher to record the rhythm of discourse by characterizing teacher and student speech as teacher presentation (TP), teacher guided authoritative discussion (AD), teacher guided dialogic discussion (DD), and student initiation (SI). The Student Motivation Towards Science Learning survey was administered to 67 students before and after a class in which argumentation was used. Analysis of interviews showed teachers collaborated to plan argumentation. Analysis of discourse identified the characteristics of argumentation and provided evidence of students' engagement in argumentation in a range of contexts. Student motivation scores were tested using Wilcoxon signed rank tests and Mann-Whitney U-tests, which showed no significant change. However, one construct of motivation 'active learning strategy' significantly increased. Quantitative findings also indicate that teachers' use of multiple methods in teaching science can affect various constructs of students' motivation. This study promotes social change by providing teachers with insight about how to engage all students in argumentation.

One learning problem is that public understanding of science is limited. Many people blame at least part of the problem on the predominant lecture approach for students' lack of science understanding. Current research indicates that more active instructional approaches can improve student learning in introductory undergraduate biology courses. Active learning may be difficult to implement because methods and strategies, ranging from in-class collaborative problem-solving to out of class multimedia presentations, are diverse, and sometimes difficult to implement. Merrill's First Principles of Instruction (hereafter referred to as "First Principles" or "First Principles of Instruction") provides a framework for implementing active learning strategies. This study used First Principles of Instruction as a framework for organizing multiple active learning strategies in a web-based module in an introductory biology course. Participants in this exploratory study were university students in Life Sciences 1350, an introductory biology course for nonscience majors. Students were randomly assigned to use either the module using First Principles of Instruction (hereafter called the First Principles module) or the module using a more traditional web-based approach (hereafter called the traditional module) as supplementary instruction. The First Principles module implemented several active learning strategies and used a progression of whole problems and several demonstration and application activities to teach the topic of "microevolution," defined as the study of how populations evolve and change over time. The traditional module implemented a more traditional web-based approach, providing information and explanations about microevolution with limited examples. This exploratory study's results showed that the learning gain from pretest to posttest at the remember level was significant for the traditional group at alpha = .05 and was significant for the First Principles group at alpha = .1. In addition the pretest to posttest gain at problem solving for the First Principles group was significant at alpha = .05. When students rated their confidence in solving future problems, those in the First Principles group were significantly more likely to predict future success at alpha = .1.

No description available.

Video classification is the task of producing a label that is relevant to the video given its frames. Active learning aims to achieve greater accuracy with fewer labeled training instances through a designed query strategy that can select representative instances from the unlabeled training instances and send them to be labeled by an oracle. It is successfully used in many modern machine learning problems. To figure out how different active learning strategies work on the video classification task, we test several active learning strategies including margin sampling, standard deviation sampling, and center sampling on Temporal Segment Networks (TSN, a classic neural network designed for video classification). We profile these three active learning strategies on systematic control experiments and get the respective models, then we compare these models’ confusion matrix, data distribution, and training log with the baseline models after the first round of query. We observe that the comparison results among models are different under different evaluation criteria. Among all the evaluation criteria we use, the average performance of center sampling is better than that of random sampling, while margin sampling and standard deviation sampling get much worse performance than random sampling and center sampling. The training log and data distribution indicate that margin sampling and standard deviation are prone to select outliers inside the data which are hard to learn but apparently not helpful to improve the model performance. Center sampling will easily outperform random sampling by F1-score. Therefore, the evaluation criteria should be formulated according to the actual application requirements. / Videoklassificering är uppgiften att producera en etikett som är relevant för videon uifrån videons bildsekvens. Aktivt lärande syftar till att uppnå större noggrannhet med färre märkta träningsexempel genom en designad frågestrategi för att välja representativa instanser som ska märkas av ett orakel från de omärkta träningsexemplen, och används framgångsrikt i många moderna maskininlärningsproblem. För att ta reda på hur olika aktiva inlärningsstrategier fungerar på videoklassificeringsuppgifter testar vi flera aktiva strategier inklusive marginalsampling, standardavvikelsessampling samt sampling baserat på Temporal Segment Networks (TSN, som är ett klassiskt neuralt nätverk designat för videoklassificeringsuppgift). Vi testar dessa tre aktiva inlärningsstrategier på systematiska kontrollexperiment, sedan jämför vi dessa modellers förvirringsmatris, datamängdsdistribution, träningslogg med baslinjemodellens efter den första frågeomgången. Vi observerar att endast metoden ”urval av centra” överträffar slumpmässigt urval. Metoden med slumpmässiga provtagningar samt metoden med är benägna att välja extremvärden som är svåra att lära sig men tydligen inte till hjälp för att förbättra modellens prestanda.

No description available.

Universities make claims about student learning that graduates don't often achieve and are under pressure to show improvement in teaching and learning in their undergraduate programs. This has been the constant focus of university-level professional development programs, but most teachers are still not using the most effective teaching methods. Individual departments need to find ways to help their instructors overcome three main challenges associated with adopting more effective student-centered teaching methods. No matter what strategy is adopted, instructors need considerable support to 1) change their beliefs about what constitutes effective teaching and learning, 2) learn to effectively implement new strategies, and 3) help their students change their beliefs about teaching and learning. We investigated whether M.S. Geoscience Education graduate students could offer the support instructors need to overcome the challenges listed above. We successfully piloted this approach during 2006 to 2008. Receiving consistent and individualized support from a Geoscience Education graduate student, the instructor changed his beliefs about teaching and learning and learned to effectively implement active learning strategies. His teaching satisfaction and student ratings also increased. Advantages of our approach include 1) the time the graduate student devoted to making course changes, 2) the consistent support the instructor received which allowed him to transfer research supported educational theory into his teaching practice, and 3) the instructor is now a departmental resource that other instructors can go to for guidance. Disadvantages include 1) the graduate student's lack of experience as a teaching consultant and 2) the difficulty of transforming a professor/student relationship into a client/consultant relationship.

This thesis focuses on a year-long project of implementing active learning in undergraduate dynamics courses at Cal Poly San Luis Obispo from 2012-2013. The purpose is to increase conceptual understanding of critical dynamics concepts and to repair misconceptions of the students. Conceptual understanding in Dynamics is vital to understanding the big picture, building upon previous knowledge, and better understanding the behavior of engineering systems. Through various hands-on activities, students make predictions, test their conceptions, and solve real world problems. These active learning methods allow students to improve their learning of Dynamics concepts. Education research on active learning is present in Physics and Mathematics disciplines, yet is still growing in Engineering. Four Inquiry-Based Learning Activities (IBLAs) and two Model-Eliciting Activities (MEAs) are discussed in this thesis. Inquiry-Based Learning Activities feature student prediction and experimentation in which the physical world acts as the authority. On the other hand, Model-Eliciting-Activities prompt students to solve real world problems and deliver results to a client. From the results, some activities yield an increase in conceptual understanding, as measured by assessment items, while others do not yield a significant increase. These activities not only help to promote conceptual gains, but also to motivate students and offer realistic engineering contexts. In conclusion, the six total IBLA and MEAS will continue in practice and be improved in their implementation. This thesis work will contribute to engineering education research of active learning methods, and improve the undergraduate dynamics curriculum locally at Cal Poly.

With the Internet of Things paradigm, the data generated by the rapidly increasing number of connected devices lead to new possibilities, such as using machine learning for activity recognition in smart environments. However, it also introduces several challenges. The sensors of different devices might be of different types, making the fusion of data non-trivial. Moreover, the devices are often mobile, resulting in that data from a particular sensor is not always available, i.e. there is a need to handle data from a dynamic set of sensors. From a machine learning perspective, the data from the sensors arrives in a streaming fashion, i.e., online learning, as compared to many learning problems where a static dataset is assumed. Machine learning is in many cases a good approach for classification problems, but the performance is often linked to the quality of the data. Having a good data set to train a model can be an issue in general, due to the often costly process of annotating the data. With dynamic and heterogeneous data, annotation can be even more problematic, because of the ever-changing environment. This means that there might not be any, or a very small amount of, annotated data to train the model on at the start of learning, often referred to as the cold start problem. To be able to handle these issues, adaptive systems are needed. With adaptive we mean that the model is not static over time, but is updated if there for instance is a change in the environment. By including human-in-the-loop during the learning process, which we refer to as interactive machine learning, the input from users can be utilized to build the model. The type of input used is typically annotations of the data, i.e. user input in the form of correctly labelled data points. Generally, it is assumed that the user always provides correct labels in accordance with the chosen interactive learning strategy. In many real-world applications these assumptions are not realistic however, as users might provide incorrect labels or not provide labels at all in line with the chosen strategy. In this thesis we explore which interactive learning strategies are possible in the given scenario and how they affect performance, as well as the effect of machine learning algorithms on performance. We also study how a user who is not always reliable, i.e. that does not always provide a correct label when expected to, can affect performance. We propose a taxonomy of interactive online machine learning strategies and test how the different strategies affect performance through experiments on multiple datasets. The findings show that the overall best performing interactive learning strategy is one where the user provides labels when previous estimations have been incorrect, but that the best performing machine learning algorithm depends on the problem scenario. The experiments also show that a decreased reliability of the user leads to decreased performance, especially when there is a limited amount of labelled data.

In this thesis, we focus on building reliable hybrid human-machine classifiers to be deployed in cost-sensitive classification tasks. The objective is to assess ML quality in hybrid classification contexts and design the appropriate metrics, thereby knowing whether we can trust the model predictions and identifying the subset of items on which the model is well-calibrated and trustworthy. We start by discussing the key concepts, research questions, challenges, and architecture to design and implement an effective hybrid classification service. We then present a deeper investigation of each service component along with our solutions and results. We mainly contribute to cost-sensitive hybrid classification, selective classification, model calibration, and active learning. We highlight the importance of model calibration in hybrid classification services and propose novel approaches to improve the calibration of human-machine classifiers. In addition, we argue that the current accuracy-based metrics are misaligned with the actual value of machine learning models and propose a novel metric ``value". We further test the performance of SOTA machine learning models in NLP tasks with a cost-sensitive hybrid classification context. We show that the performance of the SOTA models in cost-sensitive tasks significantly drops when we evaluate them according to value rather than accuracy. Finally, we investigate the quality of hybrid classifiers in the active learning scenarios. We review the existing active learning strategies, evaluate their effectiveness, and propose a novel value-aware active learning strategy to improve the performance of selective classifiers in the active learning of cost-sensitive tasks.

No description available.