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Detection of Translocation of Cochlear Implant Electrode Arrays by Intracochlear Impedance Measurements
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In: Ear Hear (2021)
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| 2 |
Relationships between student satisfaction and assessment grades in a first-year engineering unit
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Abstract:
Monitoring the quality of teaching and learning by universities relies primarily upon a combination of feedback from formal student-evaluation surveys and the long-established measure of student-cohort performance in unit assessments. This study explores major factors that might affect the data provided by these two measures and seeks to identify potential relationships between assessment performance and each of student satisfaction and students’ engineering discipline interests. Enabling this study is a large data-set obtained over the last four years from the teaching of a first-year Engineering Mechanics unit delivered twice per year to approximately 350 students in each semester from all engineering and some of multi-science disciplines. Over these years, this unit has largely remained stable in terms of unit learning outcomes, syllabus, delivery methods and teaching staff, thereby permitting potentially robust conclusions to be drawn from analyses of the data-set. By interrogating this data-set, three questions are addressed in this paper, namely (i) Is there a correlation between academic performance and student satisfaction with the unit, (ii) Did a change in assessment weighting affect students’ overall performance, and (iii) Does student interest, as reflected by their engineering-oriented discipline choice, affect their overall assessment outcomes. The investigations presented in this paper are preliminary, focusing on four-semester studies in 2010 and 2011, adopting a broad-brush approach, in order to provide the direction to more refined and rigorous lines of enquiry using the same data to determine the efficacy of present monitoring systems for teaching and learning.The initial results show that student feedback is correlated well to their assessment performance provided that cultural bias is removed. Overall, the influence on performance of changing the assessment weighting appears to be minimal and does the students’ engineering-discipline interests.
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Keyword:
academic performance; monitoring teaching and learning; student evaluation
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URL: http://hdl.handle.net/20.500.11937/33403
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| 3 |
Deep convolutional neural networks using heterogeneous pooling for trading-off acoustic invariance with phonetic confusion,” ICASSP
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In: http://research.microsoft.com/pubs/189005/RevisedFinal-multiPooling-ICASSP2013-LiDongOssama.pdf (2013)
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| 4 |
Crosslanguage knowledge transfer using multilingual deep neural networks with shared hidden layers,” ICASSP
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In: http://research.microsoft.com/pubs/189250/DNN-MultiLingual-ICASSP2013.pdf (2013)
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| 5 |
Error back propagation for sequence training of context-dependent deep networks for conversational speech transcription
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In: http://research.microsoft.com/pubs/194345/0006664.pdf (2013)
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| 6 |
e-Review Program: An alternative online interaction for a first-year unit of Engineering Mechanics using a virtual classroom
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| 7 |
A deep architecture with bilinear modeling of hidden representations: Applications to phonetic recognition
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In: http://research.microsoft.com/pubs/157583/T-DSN-ICASSP2012.pdf (2012)
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| 8 |
Boosting attribute and phone estimation accuracies with deep neural networks for detection-based speech recognition
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In: http://research.microsoft.com/pubs/157585/DNN-Detector-ICASSP2012.pdf (2012)
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| 9 |
Pipelined back-propagation for context-dependent deep neural networks,” Interspeech
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In: http://research.microsoft.com/pubs/173312/DNN-Pipeline-Interspeech2012.pdf (2012)
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| 10 |
Context-dependent pre-trained deep neural networks for large vocabulary speech recognition
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In: http://research.microsoft.com/pubs/144412/DBN4LVCSR-TransASLP.pdf (2012)
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| 11 |
Deep neural networks for acoustic modeling in speech recognition
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In: http://www.cs.utoronto.ca/%7Ehinton/absps/DNN-2012-proof.pdf (2012)
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| 12 |
Pipelined back-propagation for context-dependent deep neural networks
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In: http://mi.eng.cam.ac.uk/~xc257/papers/Pipelined_DNN.pdf (2012)
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| 13 |
HMM adaptation using a phase-sensitive acoustic distortion model for environment-robust speech recognition
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In: http://research.microsoft.com/en-us/um/redmond/groups/srg/papers/2008-deng-icassp.pdf (2008)
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| 14 |
Acero: A discriminative training framework using N-best speech recognition transcriptions and scores for spoken utterance classification
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In: http://www1.icsi.berkeley.edu/~sibel/ICASSP2007SUC.pdf (2007)
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| 15 |
A bidirectional target-filtering model of speech coarticulation and reduction: Two-stage implementation for phonetic recognition
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In: http://research.microsoft.com/pubs/78824/BiDirectionalHDM-TransASLP2006.pdf (2006)
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| 16 |
Structured speech modeling
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In: http://research.microsoft.com/srg/papers/2006-deng-transb.pdf (2006)
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| 17 |
A generative modeling framework for structured hidden speech dynamics
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In: http://www.cis.upenn.edu/~crammer/workshop_material/Deng-NIPS2005.pdf (2005)
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| 18 |
A long-contextual-span model of resonance dynamics for speech recognition: Parameter learning and recognizer evaluation
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In: http://research.microsoft.com/pubs/77917/Paper_ParameterLearning_HTM.pdf (2005)
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| 19 |
A quantitative model for formant dynamics and contextually assimilated reduction in fluent speech
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In: http://research.microsoft.com/pubs/76833/2004-deng-icslp.pdf (2004)
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