Are we there yet? Progress in promoting independent learning in a Sixth Form College

2014 ◽  
Vol 40 (4) ◽  
pp. 452-455 ◽  
Author(s):  
David William Stoten
Keyword(s):  
Independent Learning ◽  
Sixth Form

Facilitating Effective Sixth Form Independent Learning

2021 ◽  
Author(s):  
Andrew K. Shenton
Keyword(s):  
Research Question ◽  
Interaction Model ◽  
Independent Learning ◽  
Classroom Teaching ◽  
Learning Skills ◽  
Teaching Programme ◽  
Essential Resource ◽  
Set Up ◽  
Academic Lives ◽  
Sixth Form

For young people who have opted to continue their education post-sixteen, it is difficult to overstate the importance of the independent learning which takes place in the Sixth Form. Typically, the balance between classroom teaching and private study shifts dramatically at this point and individuals who intend to go on to university find themselves having to put in place strategies that will stand them in good stead for the next stage of their academic lives, in addition to serving their current needs.<br><br><i>Facilitating Effective Sixth Form Independent Learning</i> is a comprehensive guide for educators looking to support independent learning in the Sixth Form. It takes the reader on a step-by-step journey showing how an appropriate teaching programme may be set up and offers proven tools and strategies that can be adopted in the classroom. The book advises on how a worthwhile research question may be formulated and establishes the importance of teaching unifying methodologies, in addition to individual techniques, before various means of finding information are identified. It develops an approach to help students think systematically about the available options and considers methods for evaluating information and managing time. The book then addresses the construction of essays and reports and then guides readers through understanding and implementing the Information/Writing Interaction Model (IWIM). Further coverage includes strategies for countering plagiarism and numerous suggestions for promoting student reflection.<br><br>Rigorous yet accessible and featuring numerous practical examples, <i>Facilitating Effective Sixth Form Independent Learning</i> is an essential resource for educators working in a world where developing independent learning skills is not an option, but essential.

MARKOV MATHEMATICAL MODEL OF DYNAMIC ADAPTIVE TESTING OF AN ACTIVE AGENT

2018 ◽  
pp. 29-35
Author(s):  
N. V. Brovka ◽  
P. P. Dyachuk ◽  
M. V. Noskov ◽  
I. P. Peregudova
Keyword(s):  
Mathematical Model ◽  
Finite State Machine ◽  
Active Agent ◽  
Adaptive Testing ◽  
State Machine ◽  
Learning Activities ◽  
Independent Learning ◽  
Complex Object ◽  
Total Reward ◽  
Finite State

The problem and the goal.The urgency of the problem of mathematical description of dynamic adaptive testing is due to the need to diagnose the cognitive abilities of students for independent learning activities. The goal of the article is to develop a Markov mathematical model of the interaction of an active agent (AA) with the Liquidator state machine, canceling incorrect actions, which will allow mathematically describe dynamic adaptive testing with an estimated feedback.The research methodologyconsists of an analysis of the results of research by domestic and foreign scientists on dynamic adaptive testing in education, namely: an activity approach that implements AA developmental problem-solving training; organizational and technological approach to managing the actions of AA in terms of evaluative feedback; Markow’s theory of cement and reinforcement learning.Results.On the basis of the theory of Markov processes, a Markov mathematical model of the interaction of an active agent with a finite state machine, canceling incorrect actions, was developed. This allows you to develop a model for diagnosing the procedural characteristics of students ‘learning activities, including: building axiograms of total reward for students’ actions; probability distribution of states of the solution of the problem of identifying elements of the structure of a complex object calculate the number of AA actions required to achieve the target state depending on the number of elements that need to be identified; construct a scatter plot of active agents by target states in space (R, k), where R is the total reward AA, k is the number of actions performed.Conclusion.Markov’s mathematical model of the interaction of an active agent with a finite state machine, canceling wrong actions allows you to design dynamic adaptive tests and diagnostics of changes in the procedural characteristics of educational activities. The results and conclusions allow to formulate the principles of dynamic adaptive testing based on the estimated feedback.

Feedback as a form of control in a technogenic educational environment

2020 ◽  
pp. 44-49
Author(s):  
I. V. Kharlamenko ◽  
V. V. Vonog
Keyword(s):  
Foreign Language ◽  
Peer Assessment ◽  
Educational Environment ◽  
Educational Process ◽  
Mobile Technologies ◽  
Independent Learning ◽  
Bring Your Own Device ◽  
Self Assessment ◽  
Automated Control ◽  
Wide Range

The article is devoted to control and feedback in foreign language teaching in a technogenic environment. The educational process is transformed in terms of the implementation and active use of digital technologies. ICT-rich environment provides new models of interaction between the teacher, students and digital tools. It also enriches the diversity of tasks and expands the range of possible forms of control and feedback. According to the authors, automated evaluation takes place both in out-of-classroom activities and directly in the classroom using Bring Your Own Device technology (BYOD). Automated control contributes to the intensity of the educational process. It provides all the participants with an opportunity to choose a convenient mode of work and get instant feedback, thereby allowing self-assessment and self-reflection of their own actions. When teaching foreign languages, special attention should be paid to chatbot technology. Chatbots imitate human actions and are able to perform standard repetitive tasks. The growing popularity of bots is explained by a wide range of usage spheres and the ability to integrate chatbots into social networks and mobile technologies. In the technogenic educational environment, ICT can be the basis for interaction, co-editing and peer assessment in collaborative projects. In this case, students receive feedback not only from the teacher, but also from other students, which increases the motivation for independent learning. Thus, automated control, self-assessment and peer assessment can both identify problem areas for each student and design an individual learning path, which increases the effectiveness of learning a foreign language.

Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

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