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  • Technology Changing the Way We Define Learning

    The way we live, communicate, educate, and learn has changed dramatically throughout the years due to technological advancements. The way we learn has been forever altered by technological advancements. It can't be refuted. You would not be reading this if not for technology. "Our civilization is evolving now," Prensky argues. "Those parts of the world that act quickly and effectively to introduce a new educational paradigm will be the ones where children prosper and thrive in the future." And those that do not will be left behind on the road to human growth. While opinions differ on whether this is a good or bad thing, there is no doubting that learning has altered dramatically in the digital age. Technology has also increased the possibilities for communication and collaboration. Classrooms have traditionally been rather isolated, with collaboration confined to other students in the same classroom or building. With this in mind, we will go ahead to know more about how technology has become an essential tool when it comes to learning. Integrated Learning System Education should not be something you do at a specific school for a set amount of time in order to receive a certificate. Instead, that should be a lifelong voyage of learning, self-discovery, and liberation fueled by inner motivation. The quick explanation is that technology has altered practically every aspect of how we process data. Technology has been so ingrained in our daily lives that we no longer notice it. In most cases, technology has altered our perceptions of education in terms of place, time, and possibility. With innovation and technology, kids have the ability to learn at any time: they have access to resources, a device, and materials, and they can learn. It's distressing that so few programs emphasize educating young minds on how to successfully learn and unlearn. Because of the impact of digital technologies in the post-industrial era, we must be agile and adaptable to the unanticipated repercussions of disruption. Throughout our lifetimes, we may be needed to learn new abilities and mindsets on the fly and discard those that are no longer essential. These integrated lifelong learning systems may also be important in addressing the numerous sources of discontent and mental health disorders that exist in today's society. How has technology changed the roles of a teacher and a student? In a technologically rich setting, when students have access to devices, digital resources outside of the classroom, and connectivity to other folks, the educator no longer would have to be the center of learning, the one who knows almost everything, providing information to the pupils. Technology can be used for assessment in a variety of ways, but in general, it can help schools improve assessment by allowing them to create adaptable assessment criteria that support a wide range of skills and competencies. Instead of knowing everything, they can now also become even more of a guide who directs learners and certainly provides activities for experiential learning. Learning becomes much more impactful and effective when interactions are tailored to the needs of the students, which is fantastic. Because we observe a rapidly changing world in kids' technology-based learning environments, where they are no longer just consumers of knowledge, the learner transforms now as well. From Passive to Brain-based Learning Learners are often no longer only content-receptors who scribble notes or listen to teachers talk all day long on end. Mobile apps are commonly used by these active learners to study and learn whenever and wherever they can. They use the internet to get direct access to information and, in the end, to solve an issue. The most noticeable application of technology is that it allows students to learn outside of the four walls of the classroom, resulting in a real learning curve here between home and school. As a result, technology becomes a tool for active learning. Most significantly, the internet gives them the ability to learn along their own pathways. The internet and technology, for example, help to break down barriers between ideas and perspectives. Students are suddenly being exposed to stuff we've never seen before. As a result, it is inspiring new ideas, new modes of thinking, and these incredible things that are flowing out of people and pupils. Wrapping Up! Staying conscious of the impact of technology on our own learning process is one method to ensure that we stay inventive. Classroom walls are no longer a barrier, as technology allows for diverse learning needs, communication, and collaboration. It must be cultivated more, and there must be more talks about how to communicate in what seems like a way that is both creative and insightful, while also being good to others and not just a reactionary reaction. Scientists are discovering more about the mind, brain, and learning process as a result of the growing field of neuroeducation. These advancements in our understanding of how our brains work could have a significant impact on our ability to learn.

  • Future of E learning

    INTRODUCTION In the past few years, the concept of traditional education has undergone fundamental changes. Immersive classrooms are no longer the only learning option-at least with the rise of the Internet and new technologies, this learning method is no longer the only option. Nowadays, as long as you can use a computer, you can get quality education anytime, anywhere. We are entering a new era-the revolution of online education. According to HubSpot, 75% of the professionals surveyed prefer to use online training videos to learn; similarly, 68% of professionals trust their online learning platform of choice. For employees, online learning is essential to their career development. Online learning provides opportunities to achieve these goals while balancing their work and schedule. Online learners can develop their skills at their own pace and keep learning records for future reference. In this way, they can obtain education and knowledge while performing their duties. Benefit of Online learning is: Online learning is about accessibility and inclusiveness. By allowing students to work almost anywhere, it removes the physical barriers that prevent many people from achieving academic or professional ambitions. In addition, online learning can ensure that all children receive a top-notch education. Reasons for Online Learning: Flexibility: The ability to study online allows you to study anywhere freely without having to attend classes at a designated time. For people, this is usually more convenient because you don’t have to sacrifice most of the day to commute or get stuck in traffic to attend class. Online learning means you can study in the comfort of your home. A better learning experience: Given that students are allowed to study at their own pace and are encouraged to gain hands-on experience in reciting textbooks, online learning can provide a better learning experience. It can also help students to take classes from a comfortable place, which makes them more likely to learn with a positive attitude. Commuting, school time and lack of personal timetable pressure make students more energy to participate in online courses. Make education accessible: online learning allows you to receive education later in life, for example, if you work full-time but want to pursue a field of study. It allows you to receive education without having to resign or delay between them. Learn at your own place: in a more conventional education, the instructor often overwhelms you and may have difficulty trying and processing these concepts without having to walk away and spend some time trying to decipher the notes. Online learning allows you to learn on your own, so you can spend all your time mastering ideas before you continue. This not only ensures that you fully understand what you are learning, but you can also get the most out of your education. Career opportunities: Online learning teaches you to be independent. You are only responsible for your own learning. Therefore, you must coordinate your learning time and therefore force you to improve your time management skills. Being able to prove that you have ownership is a good example that can be used when answering questions in a job interview. Working on something and sticking to it till the end is a skill that many employers want.

  • Machine Learning VS Natural Language Processing

    Introduction: Various technological advancements have had an enhancing transformative effect on our daily lives. Emerging technologies like Artificial Intelligence, Cyber Security, Machine Learning, Natural Language Processing, etc; are becoming increasingly prevalent in our lives. The products of such advancements are seen in the form of new effective features in smartphones and laptops, automation devices, etc. One of the most discussed technologies is Artificial Intelligence. Such emerging disciplines are very advanced and intricate. Some terms/domains like Machine Learning, Natural Language Processing, etc; are used synonymously. However, such domains are to be well understood and differentiated. This article is aimed at making one understand the fundamental differences between Machine Learning and Natural Language Processing. Machine Learning(ML) and Natural Language Processing(NLP) are both sub-domains of Artificial Intelligence(AI) which is a branch of Computer Science(CS). The Venn diagram depicted below illustrates the relationship among five main domains, namely, CS, AI, ML, DL, and NLP. CS = Computer Science AI = Artificial Intelligence ML = Machine Learning DL = Deep Learning NLP = Natural Language Processing Artificial Intelligence: Artificial Intelligence is the branch of Computer Science that is concerned with machines or systems that are capable of cognitive abilities, rational thinking, and decision-making abilities that resemble human intelligence. It is a wide domain consisting of various concepts and other sub-domains. Machine Learning and Natural Language Processing are sub-sets of Artificial Intelligence. Machine Learning: What is Machine Learning? Machine Learning can be considered as a form of applied statistics. This sub-field of Artificial Intelligence makes use of various statistical techniques to build applications that learn automatically from data provided in the form of structured observations. The main aim of a machine learning algorithm is to make an application learn autonomously via experience in order to improve accuracy for a specific task. The Machine Learning algorithms are trained to find certain characteristic patterns in the data(training data) provided to it and this learning is used to make decisions and predictions on unseen/new data. The better the algorithm, the large and more varied the training data is, the better is the learning, and the more accurate would be the decisions and predictions for the new required data. Basic Steps in building a Machine Learning Application: Obtaining/Collecting Data The fundamental step in developing a Machine Learning Model is to collect required and appropriate data. More is the quantity of data and highly varied is the data, more accurate would be the predictions obtained or decisions made. Preparing Data Real-world data is quite noisy and may have several unnecessary elements. It is extremely crucial to provide data in an organized format for proper training of the model. Training a model on the data using an algorithm Based on the kind of problem and the output labels, an appropriate algorithm is to be chosen using which the model can be trained on the training data. Testing the model Post successful training of the model, the model can be used to make required predictions on unseen/new data. Testing measures like Accuracy, True Positive Rate, etc; can be used to determine how good the model is. Improving the model The process of training and testing Machine Learning models is an iterative process. The model’s results are to be reviewed and based on the observations, new techniques are to be applied to improve the model are explained as follows: Supervised Learning: One of the most common types of learning. Used when the data is labeled data or in other words, when the output is definite. Can further be categorized into two types: Regression(Output Label = Continuous) Classification(Output Label = Discrete) Examples: Decision Trees, Naïve Bayes, KNN algorithms. Unsupervised Learning: Used when the data is not labeled. Allows the model to work on its own to discover patterns and information that were not previously detected. Types: Clustering(To find natural groups/clusters in data) Association(To establish association between data objects) Examples: K-Means, PCA algorithms. Semi-supervised Learning: Makes use of both labeled and unlabeled data for training. Generally uses a combination of a small amount of labeled data and a large amount of unlabeled data. Examples: Self Training, Mixture models. Reinforcement Learning: Reinforcement Algorithms learn to react to a specific environment. Learning is done based on interaction with and feedback from the environment. Examples: Markov Decision Process, Q-learning algorithms. Natural Language Processing: What is Natural Language Processing? Natural Language Processing is the sub-field of Artificial Intelligence that is concerned with giving a machine the ability to understand and interpret natural human language. It creates an application/model on the basis of computational linguistics, machine learning, and deep learning techniques. The basic aim of a Natural Language Processing application is to process and understand human language that may be provided in the form of text or voice. Machine Learning models are often a part of building a Natural Language Processing application. Basic Steps in building a Natural Language Processing application: As mentioned before, the input data for an NLP application can be in the form of text or voice. There are mainly six basic steps followed that help the machine to understand the human language input. In the case of voice data as input, Phonological analysis is done where the analog acoustic signals are converted to digital signals and these digital signals are structured systematically for analysis. The text-based input is firstly tokenized into segments for further analysis. Morphological Analysis The root word of the term morphology is the Greek word ‘morph’ meaning shape. This step of NLP is concerned with distinguishing the atomic/primitive words in the input. Compound words would be analyzed by dividing them into basic distinct parts like prefix, suffix, etc. Lexical Analysis This step in NLP is concerned with the identification and analysis of the structures of the words. Syntactic Analysis This step in NLP is concerned with the identification and analysis of the grammatical structures and relationships amongst the distinct terms/words. 1. Semantic Analysis This stage in NLP is concerned with the interpretation of the probable meaning based on the interactions between different words in a sentence. 2. Disclosure Integration This stage of NLP focuses on interpreting the meaning of a piece of text based on relationships and connections between different sentences. 3. Pragmatic Analysis This step of NLP is focused on analyzing and extending the meaning of some text based on features that may not be explicit. This step is quite intricate and requires external world knowledge to analyze the implicit intentions or ideas being conveyed implicitly via the text. Components of NLP: Natural Language Generation Transforms structured data into text. Uses ML, DL techniques to generate natural language. Natural Language Understanding Transforms text into structured data. Focuses on how unstructured data is rearranged so that machines would be able to understand and interpret it. Brief Summary: Machine Learning techniques are used to build applications that learn based on data and experience. Natural Language Processing techniques are used to understand and analyze natural human language. Machine Learning and Natural Language Processing can be used to create powerful applications that can learn via proper interpretation and analysis of natural languages.

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