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action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/evelyntest/public_html/wp-includes/functions.php on line 6121Using this method a teacher designs the course, sequences its components, and includes assessments to guide learners through the learning process. The system operates based on the instructor\u2019s design and accordingly offers feedback to students based on a variety of parameters called the <\/span>Adaptivity Factors<\/b>. \u00a0To clarify, these factors have been described in-detail here.<\/span><\/span><\/p>\n
This method tells the technology what alternative course content to offer in unique situations following the \u201cif-this-then-that\u201d approach. For instance, if a student is facing difficulty grasping the concept of Bubble sorting technique in Java, then the system directs them to more quizzes on coding bubble sort rather than pushing them towards the next content (i.e., selection sort).<\/span><\/p>\n
Consequently, this method rejects linear course sequencing, provides remediation to struggling students, and brings up advanced materials for educating learners. It gives the teacher much needed agency and control over the learning process so as to ensure equity in teaching.<\/span><\/p>\n
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Based on these answers, the algorithm can adjust the path and pace so that students learn <\/span>the right thing at the right time<\/span><\/i>. One benefit reaped from adaptive learning as emphasized on our previous blog is that it respects and utilizes learners\u2019 prior knowledge.<\/span><\/span><\/p>\n
One such algorithm, for instance, is<\/span> <\/span>Bayesian Knowledge Tracing<\/span><\/a><\/span> (BKT) which measures such parameters as the probability of students demonstrating a skill correctly or incorrectly, and other such parameters.\u00a0<\/span><\/p>\n
In order to personalize the syllabi, adaptive learning systems take a variety of inputs about the learners. These factors help the system\/educator adapt to learners\u2019 styles and needs and these are called <\/span>Adaptivity factors.<\/span><\/i> Some common adaptivity factors are:\u00a0<\/span><\/span><\/p>\n
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Measuring a learner\u2019s accuracy over a series of tasks, sequenced in an increasing order of difficulty.<\/span><\/p>\n
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Discerning whether the student learns better with audio-visual content or prefers printed material.<\/span><\/p>\n
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Collecting a learner\u2019s background information: where the learner has grown up,\u00a0 their\u00a0 cultural background, and other such data points contribute to their prior knowledge. For instance, a learner based in the US is expected to excel in a module on US polity while a student hailing from another country might need added course material.<\/span><\/p>\n
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Checking the level of mastery\/skill demonstrated by learners in various topics.<\/span><\/p>\n
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Evaluating the learning prejudices or misconceptions that might be embedded in learners.<\/span><\/p>\n
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Discerning how long learners take to complete a test or what their feedback was of the same.<\/span><\/p>\n
Education technology uses a combination of any of these adaptivity factors to readjust course sequences. For instance, a combination of <\/span>Misconceptions <\/span><\/i>and <\/span>Demographics<\/span><\/i> is chosen, and the system then tests a learner\u2019s gaps in learning due to misconceptions, and aims to fill them by offering alternative reading lists and\/or tutorials.<\/span><\/span><\/p>\n
Since <\/span>Adaptivity factors<\/span><\/i> generate unique responses from each learner, they enable educators to re-route the learning experience by taking into account the learners\u2019 experiences, instead of blindly moving forward with a set course structure.\u00a0<\/span><\/span><\/p>\n
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The overall learning algorithm involved in <\/span>Adaptive Learning<\/span><\/i> has been shown below:\u00a0<\/span><\/span><\/p>\n
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