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- Creators: Arizona State University
Description
From the instructional perspective, the scope of "active learning" in the literature is very broad and includes all sorts of classroom activities that engage students with the learning experience. However, classifying all classroom activities as a mode of "active learning" simply ignores the unique cognitive processes associated with the type of activity. The lack of an extensive framework and taxonomy regarding the relative effectiveness of these "active" activities makes it difficult to compare and contrast the value of conditions in different studies in terms of student learning. Recently, Chi (2009) proposed a framework of differentiated overt learning activities (DOLA) as active, constructive, and interactive based on their underlying cognitive principles and their effectiveness on students' learning outcomes. The motivating question behind this framework is whether some types of engagement affect learning outcomes more than the others. This work evaluated the effectiveness and applicability of the DOLA framework to learning activities for STEM classes. After classification of overt learning activities as being active, constructive or interactive, I then tested the ICAP hypothesis, which states that student learning is more effective in interactive activities than constructive activities, which are more effective than active activities, which are more effective than passive activities. I conducted two studies (Study 1 and Study 2) to determine how and to what degree differentiated activities affected students' learning outcomes. For both studies, I measured students' knowledge of materials science and engineering concepts. Results for Study 1 showed that students scored higher on all post-class quiz questions after participating in interactive and constructive activities than after the active activities. However, student scores on more difficult, inference questions suggested that interactive activities provided significantly deeper learning than either constructive or active activities. Results for Study 2 showed that students' learning, in terms of gain scores, increased systematically from passive to active to constructive to interactive, as predicted by ICAP. All the increases, from condition to condition, were significant. Verbal analysis of the students' dialogue in interactive condition indicated a strong correlation between the co-construction of knowledge and learning gains. When the statements and responses of each student build upon those of the other, both students benefit from the collaboration. Also, the linear combination of discourse moves was significantly related to the adjusted gain scores with a very high correlation coefficient. Specifically, the elaborate type discourse moves were positively correlated with learning outcomes; whereas the accept type moves were negatively correlated with learning outcomes. Analyses of authentic activities in a STEM classroom showed that they fit within the taxonomy of the DOLA framework. The results of the two studies provided evidence to support the predictions of the ICAP hypothesis.
ContributorsMenekşe, Muhsin (Author) / Chi, Michelene T.H. (Thesis advisor) / Baker, Dale (Committee member) / Middleton, James (Committee member) / Arizona State University (Publisher)
Created2012
Description
Power supply management is important for MEMS (Micro-Electro-Mechanical-Systems) bio-sensing and chemical sensing applications. The dissertation focuses on discussion of accessibility to different power sources and supply tuning in sensing applications. First, the dissertation presents a high efficiency DC-DC converter for a miniaturized Microbial Fuel Cell (MFC). The miniaturized MFC produces up to approximately 10µW with an output voltage of 0.4-0.7V. Such a low voltage, which is also load dependent, prevents the MFC to directly drive low power electronics. A PFM (Pulse Frequency Modulation) type DC-DC converter in DCM (Discontinuous Conduction Mode) is developed to address the challenges and provides a load independent output voltage with high conversion efficiency. The DC-DC converter, implemented in UMC 0.18µm technology, has been thoroughly characterized, coupled with the MFC. At 0.9V output, the converter has a peak efficiency of 85% with 9µW load, highest efficiency over prior publication. Energy could be harvested wirelessly and often has profound impacts on system performance. The dissertation reports a side-by-side comparison of two wireless and passive sensing systems: inductive and electromagnetic (EM) couplings for an application of in-situ and real-time monitoring of wafer cleanliness in semiconductor facilities. The wireless system, containing the MEMS sensor works with battery-free operations. Two wireless systems based on inductive and EM couplings have been implemented. The working distance of the inductive coupling system is limited by signal-to-noise-ratio (SNR) while that of the EM coupling is limited by the coupled power. The implemented on-wafer transponders achieve a working distance of 6 cm and 25 cm with a concentration resolution of less than 2% (4 ppb for a 200 ppb solution) for inductive and EM couplings, respectively. Finally, the supply tuning is presented in bio-sensing application to mitigate temperature sensitivity. The FBAR (film bulk acoustic resonator) based oscillator is an attractive method in label-free sensing application. Molecular interactions on FBAR surface induce mass change, which results in resonant frequency shift of FBAR. While FBAR has a high-Q to be sensitive to the molecular interactions, FBAR has finite temperature sensitivity. A temperature compensation technique is presented that improves the temperature coefficient of a 1.625 GHz FBAR-based oscillator from -118 ppm/K to less than 1 ppm/K by tuning the supply voltage of the oscillator. The tuning technique adds no additional component and has a large frequency tunability of -4305 ppm/V.
ContributorsZhang, Xu (Author) / Chae, Junseok (Thesis advisor) / Kiaei, Sayfe (Committee member) / Bakkaloglu, Bertan (Committee member) / Kozicki, Michael (Committee member) / Phillips, Stephen (Committee member) / Arizona State University (Publisher)
Created2012
Description
Since the early 2000s the Rubik’s Cube has seen growing usage at speedsolving competitions and as an effective tool to teach Science, Technology, Engineering, Mathematics (STEM) topics at hundreds of schools and universities across the world. Recently, cube manufacturers have begun embedding sensors to enable digital face tracking. The live feedback from these so called “smartcubes” enables a new wave of immersive solution tutorials and interactive educational games using the cube as a controller. Existing smartcube software has several limitations. Manufacturers’ applications support only a narrow set of puzzle form factors and application platforms, fragmenting the ecosystem. Most apps require an active internet connection for key features, limiting where users can practice with a smartcube. Finally, existing applications focus on a single 3x3x3connection, losing opportunities afforded by new form factors. This research demonstrates an open-source smartcube application which mitigates these limitations. Particular attention is given to creating an Application Programming Interface (API) for smartcube communication and building representative solve analysis tools. These innovations have included successful negotiations to re-license existing open-source Rubik’sCube software projects to support deployment on multiple platforms, particularly iOS. The resulting application supports smartcubes from three manufacturers, runs on two platforms (Android and iOS), functions entirely offline after an initial download of remote assets, demonstrates concurrent connections with up to six smartcubes, and supports all current and anticipated smartcube form factors. These foundational elements can accelerate future efforts to build smartcube applications, including automated performance feedback systems and personalized gamification of learning experiences. Such advances will hopefully enhance the Rubik’s Cube’s value both as a competitive toy and as a pedagogical tool in educational institutions worldwide.
ContributorsHale, Joseph (Author) / Bansal, Ajay (Thesis advisor) / Heinrichs, Robert (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Millimeter-wave (mmWave) and sub-terahertz (sub-THz) systems aim to utilize the large bandwidth available at these frequencies. This has the potential to enable several future applications that require high data rates, such as autonomous vehicles and digital twins. These systems, however, have several challenges that need to be addressed to realize their gains in practice. First, they need to deploy large antenna arrays and use narrow beams to guarantee sufficient receive power. Adjusting the narrow beams of the large antenna arrays incurs massive beam training overhead. Second, the sensitivity to blockages is a key challenge for mmWave and THz networks. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle both these challenges lies in leveraging additional side information such as visual, LiDAR, radar, and position data. These sensors provide rich information about the wireless environment, which can be utilized for fast beam and blockage prediction. This dissertation presents a machine-learning framework for sensing-aided beam and blockage prediction. In particular, for beam prediction, this work proposes to utilize visual and positional data to predict the optimal beam indices. For the first time, this work investigates the sensing-aided beam prediction task in a real-world vehicle-to-infrastructure and drone communication scenario. Similarly, for blockage prediction, this dissertation proposes a multi-modal wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. Evaluations on both real-world and synthetic datasets illustrate the promising performance of the proposed solutions and highlight their potential for next-generation communication and sensing systems.
ContributorsCharan, Gouranga (Author) / Alkhateeb, Ahmed (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Turaga, Pavan (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2024
Description
A reform movement in the United States has focused on STEM education and 21st century soft skills such as critical thinking, communication, collaboration, and creativity. This spotlight on STEM instruction provided an opportunity to explore how K-14 STEM teacher participants perceived a Design Thinking Instructional Problems (DTIP) approach to developing instructional lessons. The study used a convergent parallel mixed-methods design with a survey instrument and a multiple case study focused on K-14 in-service STEM teachers. Data were collected from teacher participants during two five-week summer Research Experience for Teachers (RET) programs as part of two separate National Science Foundation (NSF) funded Engineering Research Centers (ERC) located at a large southwestern university in the United States (n=16). The study was conducted over three phases. During Phase I and II, teacher participants experienced a Design Thinking Overview workshop and weekly DTIP professional development sessions to facilitate the development of an RET instructional lesson. Pre- and post-program DTIP surveys and background interviews were conducted with all teacher participants (n=16). From this original group, teacher participants were selected as cases. Implementation observations and post-implementation interviews were conducted with these case-teachers (n=10). The study included frequency analysis and descriptive statistics of survey data. Qualitative data were analyzed using direct interpretation, thematic analysis, and open coding with the constant comparative method. A variety of arrays, summaries, and matrices were used to visualize patterns across and within individual case-teacher results. All 16 teacher participants viewed themselves as designers solving complex instructional problems. All 16 teacher participants found the DTIP professional development sessions to have somewhat to very much provided additional value during their RET summer programs. Six of the 10 case-teachers perceived the DTIP model graphic as mostly to completely corresponding to the way in which they developed their RET instructional lesson. Lastly, eight of the 10 case-teachers chose to embed a Design Thinking student learning strategy into the RET instructional lesson they developed.
ContributorsElwood, Kristin (Author) / Savenye, Wilhelmina (Thesis advisor) / Jordan, Michelle E (Committee member) / Henriksen, Danah (Committee member) / Mishra, Punya (Committee member) / Arizona State University (Publisher)
Created2018
Description
The science, technology, engineering, and math (STEM) education community is interested in using virtual reality (VR) to help students learn STEM knowledge. Prior research also provided evidence that VR learning can increase students’ motivation and learning achievement. However, it was not clear whether the effect of VR on learning was partly from sensory novelty and whether the effectiveness was sustainable. This study was to satisfy the concern on the sustainability of VR STEM learning in instruction and address the research gaps in exploring the effect of VR on a STEM learning experience with a consideration of novelty.
The study used a mixed-methods experimental design and involved a three-session VR STEM learning intervention. The quantitative data was collected through the intervention by survey questionnaire, session quiz, and pre- and post-tests, while the interviews were taken after the intervention. The structural equation modeling method was used to explore the relationships among factors in the VR learning experience. Longitudinal quantitative comparisons were conducted with the multiple imputation method. Its purpose was to evaluate the changing magnitude of factors across sessions. After quantitative analysis, interview transcripts were analyzed. They were used to triangulate or provide context for understanding of quantitative results.
The results showed that motivation and engagement play a critical mediation role in an effective VR learning experience. While individuals’ psychological responses and motivation may significantly increase in a VR learning experience for novelty, the novelty effect may not steeply decrease when individuals are becoming familiar with the novelty. This phenomenon is more observable in a VR condition having a high degree of immersion and embodiment. In addition, novelty does not necessarily increase learning achievement. The increase of learning achievement is more dependent on a match between the learning content and the learning method. The embodied learning method is appropriate for instructing difficult knowledge and spatial knowledge. Reserving enough time for reflection is important to deep learning in a VR environment.
The study used a mixed-methods experimental design and involved a three-session VR STEM learning intervention. The quantitative data was collected through the intervention by survey questionnaire, session quiz, and pre- and post-tests, while the interviews were taken after the intervention. The structural equation modeling method was used to explore the relationships among factors in the VR learning experience. Longitudinal quantitative comparisons were conducted with the multiple imputation method. Its purpose was to evaluate the changing magnitude of factors across sessions. After quantitative analysis, interview transcripts were analyzed. They were used to triangulate or provide context for understanding of quantitative results.
The results showed that motivation and engagement play a critical mediation role in an effective VR learning experience. While individuals’ psychological responses and motivation may significantly increase in a VR learning experience for novelty, the novelty effect may not steeply decrease when individuals are becoming familiar with the novelty. This phenomenon is more observable in a VR condition having a high degree of immersion and embodiment. In addition, novelty does not necessarily increase learning achievement. The increase of learning achievement is more dependent on a match between the learning content and the learning method. The embodied learning method is appropriate for instructing difficult knowledge and spatial knowledge. Reserving enough time for reflection is important to deep learning in a VR environment.
ContributorsHuang, Wen (Author) / Roscoe, Rod (Thesis advisor) / Johnson, Mina (Committee member) / Craig, Scotty (Committee member) / Arizona State University (Publisher)
Created2020
Description
This is a film review of the German film Run Lola Run, released in 1988.
ContributorsMesch, Claudia (Author)
Created2000