Making Teaching and Learning Effective Using Analytics

2021 ◽  
pp. 004723952110638
Author(s):  
Seifeddine Besbes ◽  
Bhekisipho Twala ◽  
Riadh Besbes
Keyword(s):  
Classroom Environment ◽  
Teaching And Learning ◽  
Teaching Effectiveness ◽  
State Of The Art ◽  
Immune Recognition ◽  
Artificial Immune ◽  
Empirical Comparison ◽  
Academic Context ◽  
Observation Instrument ◽  
Recognition Systems

In this paper, an empirical comparison of three state-of-the-art classifier methods (artificial immune recognition systems, Lazy-K Star, and random tree) to predict teachers’ ability to adapt in a classroom environment is carried out. Two educational databases are used for this task. First, measures collected in an academic context, especially from classroom visits, are used (database 1). Then, the three classifiers quantify the acts, behaviors, and characteristics of teaching effectiveness and the teacher’s “ability to adapt in the classrooms.” Professional classrooms visits to more than 200 teachers are used as the second database (database 2). An interactive grid gathering 63 educational acts and behaviors is conceived as an observation instrument for those visits. Within the Waikato Environment for Knowledge Analysis library environment, and with the progressive enhancement of the raw database, the utilization of state-of-the-art classification methods when predicting teaching effectiveness shows promising results, especially when data quality issues are considered.

scholarly journals Using Artificial Immune Recognition Systems in Order to Detect Early Breast Cancer

2013 ◽  
Vol 5 (2) ◽  
pp. 34-40 ◽  
Author(s):  
C.D. Katsis ◽  
I. Gkogkou ◽  
C.A. Papadopoulos ◽  
Y. Goletsis ◽  
P.V. Boufounou
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Immune Recognition ◽  
Artificial Immune ◽  
Recognition Systems

scholarly journals Technology-Assisted Teaching Aids in Teaching and Learning: Evidence from the Malaysian Tahfiz Ulul Albab Model (TMUA)

2019 ◽  
Vol 8 (12) ◽  
pp. 4401-4404
Keyword(s):  
Teaching And Learning ◽  
Teaching Effectiveness ◽  
State Of The Art ◽  
Study Data ◽  
Learning Activities ◽  
Technology Usage ◽  
Teaching Aids ◽  
Art Teaching ◽  
Teachers And Students ◽  
Service Training

The use of state-of-the-art teaching aids in teaching and learning activities (T&L) had been proven in enhancing teachers’ teaching effectiveness in the classroom. This study aims to examine the level of technology usage among teachers and students in the Tahfiz Ulul Albab Model (TMUA) program as well as teachers' perception on technology usage among teachers in the TMUA program based on 21st Century In-Service Training (LADAP PAK21). A total of 30 teachers and 100 students were selected as respondents in this study. Data analysis was performed using Statistical For Social Science (SPSS) Version 22.0 software. The findings show that the level of technology-assisted teaching aids usage is moderate to high. However, there is no difference among teachers who follow LADAP PAK21 towards technology-assisted teaching aids usage. The findings of this study show that although teachers have a positive perception of the importance of u technology usage, the level of usage is still at a moderate level. This shows that the level of awareness and encouragement in the usage of technology-assisted teaching aids needs to be increased.

Teaching Music Theory

2020 ◽  
Author(s):  
Jennifer Snodgrass
Keyword(s):  
Music Theory ◽  
Classroom Environment ◽  
Teaching And Learning ◽  
Undergraduate Research ◽  
Real Life ◽  
Aural Skills ◽  
New Faculty ◽  
Music Therapist ◽  
Starting Point ◽  
Teaching Music

Many innovative approaches to teaching are being used around the country, and there is an exciting energy about the scholarship of teaching and learning. But what is happening in the most effective music theory and aural skills classrooms? Based on 3 years of field study spanning 17 states, coupled with reflections from the author’s own teaching strategies, Teaching Music Theory: New Voices and Approaches highlights teaching approaches with substantial real-life examples from instructors across the country. The main premise of the text focuses on the question of “why.” Why do we assess in a particular way? Why are our curricula designed in a certain manner? Why should students master aural skills for their career as a performer, music educator, or music therapist? It is through the experiences shared in the text that many of these questions of “why” are answered. Along with answering some of the important questions of “why,” the book emphasizes topics such as classroom environment, undergraduate research and mentoring, assessment, and approaches to curriculum development. Teaching Music Theory: New Voices and Approaches is written in a conversational tone to provide a starting point of dialogue for students, new faculty members, and seasoned educators on any level. The pedagogical trends presented in this book provide a greater appreciation of outstanding teaching and thus an understanding of successful approaches in the classroom.

Approaches for Multilingual Phone Recognition in Code-switched and Non-code-switched Scenarios Using Indian Languages

2021 ◽  
Vol 20 (4) ◽  
pp. 1-19
Author(s):  
Manjunath K. E. ◽  
Srinivasa Raghavan K. M. ◽  
K. Sreenivasa Rao ◽  
Dinesh Babu Jayagopi ◽  
V. Ramasubramanian
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Window Size ◽  
Recognition System ◽  
Error Rates ◽  
Indian Languages ◽  
International Phonetic Alphabet ◽  
Phone Recognition ◽  
Front End ◽  
Recognition Systems

In this study, we evaluate and compare two different approaches for multilingual phone recognition in code-switched and non-code-switched scenarios. First approach is a front-end Language Identification (LID)-switched to a monolingual phone recognizer (LID-Mono), trained individually on each of the languages present in multilingual dataset. In the second approach, a common multilingual phone-set derived from the International Phonetic Alphabet (IPA) transcription of the multilingual dataset is used to develop a Multilingual Phone Recognition System (Multi-PRS). The bilingual code-switching experiments are conducted using Kannada and Urdu languages. In the first approach, LID is performed using the state-of-the-art i-vectors. Both monolingual and multilingual phone recognition systems are trained using Deep Neural Networks. The performance of LID-Mono and Multi-PRS approaches are compared and analysed in detail. It is found that the performance of Multi-PRS approach is superior compared to more conventional LID-Mono approach in both code-switched and non-code-switched scenarios. For code-switched speech, the effect of length of segments (that are used to perform LID) on the performance of LID-Mono system is studied by varying the window size from 500 ms to 5.0 s, and full utterance. The LID-Mono approach heavily depends on the accuracy of the LID system and the LID errors cannot be recovered. But, the Multi-PRS system by virtue of not having to do a front-end LID switching and designed based on the common multilingual phone-set derived from several languages, is not constrained by the accuracy of the LID system, and hence performs effectively on code-switched and non-code-switched speech, offering low Phone Error Rates than the LID-Mono system.

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