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Introduction

My name is Michael Ajao-olarinoye, a second-year PhD student in the centre of computational science and mathematical modelling; my research work is around the use of operational research techniques and epidemiology modelling to solve the problem of allocating vaccines between cities and countries. My original background was in electrical and electronics engineering; I worked in various roles that involved data analytics and predictive modelling in my early career days. I decided to pursue a master's degree in data science at Coventry university, which gave me the required skills to grow professionally for a career in data science. I wanted to research the field of big data, predictive analytics, and artificial intelligence to satisfy my curiosity while creating solutions for some of the problems faced by humanity and businesses. Coventry University offered me the platform and opportunity to develop into a data scientist. After completing my master's degree at Coventry University, I had the opportunity to work on a project close and dear to my heart as a PhD student. My daily task involves learning and growing as a research student, while I also develop my interpersonal relationship skills, based on interacting with my supervisors and colleagues.

UNDERSTANDING STUDENT LEARNING

The past week has been pretty enlightening in terms of educational approaches for me as a Teaching Assistant who recently delivered a lab session on K-means clustering. The investigation of various learning theories such as behaviourism, cognitivism, and constructivism has caused me to consider how these ideas may be integrated into the teaching of machine learning algorithms in a data science curriculum. To begin, the K-means algorithm is a widely used unsupervised learning technique in data science, suitable for grouping together comparable data points. While the method itself is mathematically simple, the actual problem is in assisting students in understanding the underlying concepts and their practical applications. This is when a combination of learning theories comes in handy.

The lab sessions use behaviourism as a foundational technique. The controlled learning environment in which students code and receive rapid feedback is intended to promote good habits. The lab exercises are divided into incremental challenges, which allow pupils to master each stage before moving on to the next. This strategy helps to simplify the difficult algorithm. Constructivism, on the other hand, shines through in group projects and flipped classroom approaches. Students are given the autonomy to create their understanding of the K-means algorithm by participating in group activities. The flipped classroom approach also allows them to prepare ahead of time, making lab sessions more engaging and problem-solving focused rather than just instruction. This is invaluable in a discipline like data science, where the final objective is to apply algorithms to real-world situations.

Cognitivism can assist pupils in understanding the 'why' underlying the algorithm. The theoretical component of the lab session encourages students to consider the logic and rationale underlying K-means. This cognitive engagement allows students to better internalise the method, allowing them to use it in a variety of scenarios. There is, however, always opportunity for development. The utilisation of real-world data sets that reflect current challenges, such as those in healthcare or the social sciences, is one feature that could be improved. This not only piques the students' attention but also assists them in comprehending the societal implications of data science. Another idea is to incorporate more formative assessments throughout the lab sessions. These could be used as checkpoints to assess students' comprehension and adjust the teaching technique accordingly.

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Management led teaching observation form

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Management led teaching observation form

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Teaching Experience - masters students Data science

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Teaching Experience - masters students Data science

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Data science teaching practice - Made with Clipchamp.mp4

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I got feedback from my student on my teaching. i was impressed to see that students responded to my teaching. i know i have to improve and develop my skills to communicate and pass down my message using the teaching techniques taught in this module.

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start stop continue feedback survey online tool

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All answers - Start Stop Continue - online tool

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Simple yet powerful technique that allows you to get feedback from your team members quickly and effectively.

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Enhancing Teaching through Diverse Evaluation and Reflection

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Enhancing Teaching through Diverse Evaluation and Reflection

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As an educator, I continually seek to enhance my instruction by evaluating my performance and soliciting input from a variety of sources. Currently, I assess my teaching via self-reflection, peer observations, and student feedback. These evaluations assist me in identifying areas for improvement and implementing modifications that enhance student learning.

I am interested in investigating collaborative learning sets (CLSs) and action learning sets (ALSs) to engage my classmates in more meaningful evaluations in the future. By participating in small groups, I can benefit from my colleagues' shared problem-solving, assistance, and insights. In addition, I'd like to investigate learning communities, which offer opportunities to collaborate with fellow educators, create networks between theory and practise, and socially construct knowledge on particular topics.

Obtaining feedback from a variety of sources enables me to obtain a thorough understanding of my teaching effectiveness. Formal and informal student feedback helps me assess my students' learning experiences and modify my teaching strategies. Peer observations and incidental data, such as classroom mood and attendance rates, allow me to reflect on my instruction in real time and make modifications to enhance the learning environment.

Lastly, by engaging in regular self-reflections on my teaching practise and continuing professional development activities, I am better able to identify areas for refinement and monitor my progress towards becoming a more effective and influential educator. I am committed to improving my teaching practise and nurturing pupil success by combining these evaluation strategies.

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In reflecting on my current approach to using space, I realize that I prioritize fostering collaboration and interaction in the classroom by arranging seating in clusters or circles. However, I acknowledge the growing importance of virtual spaces in online and blended learning contexts. To create a sense of community in these settings, I have utilized discussion forums and virtual breakout rooms, but I recognize the potential for further exploration and adaptation of my approach.

Students' perceptions of space can significantly impact their learning experiences. Consequently, it is essential to consider their diverse preferences and needs when designing both physical and virtual learning environments. By offering a variety of activities and spaces, I aim to cater to these different needs and create inclusive learning experiences.

Going forward, I intend to pilot alternative approaches to enhance my students' learning, drawing inspiration from resources like JISC's guide on technology integration and Coventry University's Online International Learning examples. By continually reflecting, evaluating, and adapting my practices, I hope to create effective learning environments that promote engagement, collaboration, and success for all students.

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Lesson Planning and Designing Active Learning

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Lesson Planning and Designing Active Learning

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My lecture plan was designed to impart a thorough comprehension of machine learning concepts and their applications. I stressed achieving a balance between theoretical knowledge and practical, hands-on experiences. After I presented my proposal to my colleagues, they provided insightful feedback. To engage students and stimulate critical thinking, they suggested incorporating more group discussions and real-world case studies. I incorporated their suggestions into my lesson plan after appreciating their input.

Regarding active learning strategies, I have always advocated for innovative teaching methods that empower students to assume responsibility for their own learning. My own teaching examples include flexible classrooms and project-based learning. Students can learn at their own pace in flipped classrooms, while project-based learning encourages them to apply their knowledge to real-world problems.

After considering various active learning approaches, I am motivated to investigate inquiry-based learning and research-informed teaching in greater depth. Enquiry-based learning will stimulate students' inquisitiveness and problem-solving skills, while research-inspired instruction will expose them to cutting-edge research and the most recent developments in machine learning.

To present these active learning strategies, I intend to:

Collaborate with colleagues and researchers to identify pertinent research topics and incorporate them into the curriculum of the course.
Construct inquiry-based, open-ended, real-world initiatives that require students to implement their machine learning knowledge.
To inspire students and demonstrate the practical applications of machine learning, arrange guest lectures by industry experts and researchers.

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I recently examined the learning outcomes for my information retrieval course. In doing so, I have endeavoured to ensure that these outcomes are lucid, measurable, and aligned with the teaching methods and assessment techniques utilised in each course.

I rewrote the original learning outcome for the Machine Learning course from "Understand various machine learning algorithms" to "Apply and compare the performance of various machine learning algorithms in different problem domains." This revised learning outcome emphasises students' active engagement with the material and encourages them to analyse and evaluate the efficacy of the algorithms they learn.

To enhance my knowledge of learning outcomes, I consulted a variety of resources. These resources provided invaluable insight into the development of effective learning outcomes and the selection of appropriate assessment methods. The revised learning outcomes emphasise knowledge application and more accurately reflect the teaching methods and assessments used in each course. This alignment ensures that students receive a consistent, meaningful, and comprehensible education throughout their studies.

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Inclusive Learning and Teaching as Good Teaching

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Inclusive Learning and Teaching as Good Teaching

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After researching the idea of inclusive learning and teaching, I've seen how important it is to creating a productive learning environment. I've been inspired to think about the components that make teaching genuinely effective and to take into account how inclusion fits into this framework by the question, "Is inclusive learning and teaching simply good teaching?".

Recognising and accepting the various backgrounds, skills, and needs of each student in the classroom are essential components of inclusive learning and teaching. By fostering a positive atmosphere, this strategy hopes to help each student realise their full potential. Instead of addressing individual adjustments as an afterthought, inclusive teaching practises frequently incorporate anticipatory curriculum design, adaptation, and a focus on fulfilling the needs of all learners.

In my view, an integral part of effective education is inclusive learning and teaching. By taking inclusive practises into account and putting them into practise, educators may meet a larger range of student needs and promote a feeling of involvement and belonging for everyone. Through inclusion, it is ensured that no student is left behind and that all learners have an equal chance to succeed.

Additionally to helping individuals who may have particular needs, inclusive teaching improves the overall educational experience for all students. For instance, having lecture material accessible online before class may benefit overseas or dyslexic students, but it also encourages a deeper comprehension of topics among all students. This method encourages students to actively interact with the topic rather than passively take notes in class.

In conclusion, after giving the subject some thought, I now firmly feel that inclusive learning and teaching are essential components of effective instruction. No matter a student's background or requirements, educators may improve the learning environment for all students by using inclusive practises. Everyone engaged eventually benefits from a better educational experience as a result of this.

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The investigation of various learning theories this week has been insightful and has prompted me to consider how I currently instruct data science and computational intelligence. As I learn more about the various ideas, I see how they have a big impact on how I teach and how I can help my pupils.

Each of behaviourism, cognitivism, and constructivism has advantages and may be used in many ways while teaching data science. For instance, behaviourism may be helpful in highlighting the significance of developing certain programming abilities through consistent practise. Contrarily, constructivism enables me to create a setting where students may build on their prior knowledge and work with others to further their understanding, while cognitivism aids in my understanding of the mental processes required in learning complicated algorithms.

The Coventry Way's emphasis on active learning fits quite well with how I approach teaching. The six Education Strategy components offer a structure that I may use in my classes. For instance, the emphasis on inclusion inspires me to create activities that accommodate a range of learning preferences and abilities, and the emphasis on cooperation and partnership stimulates the development of teamwork and collaborative problem-solving, both of which are crucial talents in data science. I use a variety of active learning techniques when I teach to keep students interested and encourage greater comprehension. Several instances include:

1. Interactive coding sessions: I provide practical classes where students get a chance to practise coding in the present, ask questions, and get feedback right away. This helps students learn from their errors and reinforces programming ideas.

2. Group projects: Students apply their theoretical knowledge and cooperate with their classmates as they work in small groups to produce data-driven solutions for actual situations.

3. Flipped classroom: I may use in-class time for discussions and problem-solving exercises by giving students pre-class readings and videos to watch. This encourages students to apply their knowledge and actively interact with the topic.

The learning theories we've looked at are intimately related to these active learning strategies. Behaviourism serves as the foundation for the interactive coding sessions since they employ reward and repetition to advance programming abilities. Constructivist concepts are embodied in group projects and the flipped classroom, where students build new information by relating it to what they already know and cooperating.

In conclusion, this week's study of learning theories and the Coventry Way has given me important new perspectives on how I approach teaching. I am better able to create a learning environment for my students in data science and computational intelligence that is more engaging, inclusive, and successful by comprehending the fundamental concepts of these theories and implementing the Coventry Way's six pillars.