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School districts continue changing education requirements, and coupled with a spike in students diagnosed as learning disabled, how will educators respond to continue assisting the growing population of special needs students? Software companies may present solutions by working to enhance the adaptive learning experience with customized learning management systems (LMS).
Under the banner of EdTech, software-based resources are used to create customized learning paths for individual students struggling with academics. Computer artificial intelligence (AI) algorithms measure a student’s comprehension, reaction time, and engagement level to offer real-time feedback that enables the content to be presented and adjusted to best suit an individual’s learning style.
Creating positive, engaging end-user experiences for students and educators is critical for tech companies because they could provide the resources school districts seek when assessing classroom needs and new strategy development to address ongoing academic challenges facing today’s students.
Adaptive learning programs paired with an optimized LMS enables educators to conveniently organize adaptive learning and e-learning lessons in one central location, which students can access and revisit to improve academic comprehension. This eliminates the traditional teaching response of reviewing lessons for the entire class, despite a limited amount of students requiring the intervention.
Shareable Content Object Reference Model (SCORM) has been the standard model for content authoring for LMSs, but developers are now also utilizing xAPI. SCORM can still be used to create unique, automated content with real-time data capture and analysis.
Customizations allow for rapid deployment via the Cloud, creating opportunities for a larger audience (classroom) to efficiently access learning paths featuring video and audio accompaniment to lesson plans. Educators who need to upload content from various sources can use SCORM because of seamless compatibility to import, deploy, and monitor content. Content packaging is based on XML and runs on ECMAScript.
Developers now look to xAPI as a more viable option for LMS enhancements. xAPI is easily integrated into an LMS and eliminates wait times with continuous feed to a Learning Record Store (LRS). Educators utilizing an LMS with xAPI unlock the unique ability to monitor a student’s learning path online and offline without being exclusively reliant on the LMS.
With an optimal LMS tailored for adaptive learning, educators can access larger quantities of pertinent data at a quicker pace, and with reduced time to access student data, educators can make academic interventions in a timely manner.
Artificial intelligence is an invaluable resource for adaptive learning because accessing predictive learning data enables educators to precisely gauge when and in what specific area a student begins struggling.
Utilizing data-driven AI within adaptive learning enables educators to achieve the following:
Compiling student data before lessons. This streamlines lesson planning to accurately identify what content students struggle with while filtering already mastered concepts.
Automated tracking of student progress. Teachers can quickly access predictive learning data to accurately gauge progress, enabling faster intervention if a student is struggling.
Analyzing data that determines a student’s level and duration of engagement and what specific content interests the student.
Reliable and objective feedback. Depending on which adaptive program a student is engaged with, the program flags the content a student struggles with and reintroduces the content or presents the content differently after an algorithm has identified the exact piece of content a student struggles with.
AI and predictive data assist educators with creating strong baseline learning profiles, which enable educators to proactively create individualized interventions and lesson plans more expeditiously than traditional, task-based assessments. An LMS powered with AI-driven features would facilitate a more personalized, engaging adaptive learning program.
Algorithms compile data for educators to make clearer assessments of student comprehension, and within adaptive engines, educators filter previously mastered content from newly introduced concepts during course design.
Many adaptive learning programs utilize frameworks featuring a learner model, content model, and adaptive engines to create customized learning paths for students. Learner models compile data such as individual performance on previous content and how long a student spends per task, which helps construct what educators call a ‘learning profile.’ Content models may utilize Natural Language Processing (NLP) to assess language comprehension and identify problematic vocabulary for students.
Depending on what adaptive learning program an educator utilizes, students may receive visual or audio directives detailing which piece of content is challenging. Additionally, NLP assesses a student’s performance within reading comprehension tasks by flagging which parts a student struggles with, and the duration spent per task. Once mastery is achieved, the program will permit the student to proceed with the lesson.
Traditional teaching data focused on task-based methods, such as cumulative scores of student work. Algorithms provide a more efficient way for educators to analyze student performance data.
Another crucial feature for an adaptive learning LMS would be gamification, leveraging digital games to include academic tasks designed to further engage students.
Educators use gamification to implement immersive lessons for students who are more visual learners and may tend to struggle with focus and engagement times.
Optimal LMS solutions enable the creation of interactive token economies implemented by educators that can be featured in digital game-based adaptive learning lessons.
A token economy is a strategy educators use to effectively manage classrooms and streamline student progress by rewarding students that meet academic or behavioral goals with tangible rewards. Within behavior modification programs, rewards are termed reinforcers.
An immersive, in-class token economy can be a very effective strategy to increase academic engagement. Educators may leverage digital game-based adaptive learning programs as reinforcers, making them a valuable resource because the reinforcer itself functions as a learning tool.
With recent acquisitions of EdTech businesses by software companies and increased e-learning industries added to software provider portfolios, adaptive learning presents a viable opportunity for software proprietors to make an impact in e-learning and the adaptive learning software market in particular.
An effective adaptive learning program paired with an optimized LMS implemented in school districts relies on a strong IT foundation, calling for design and implementation through the expertise of product engineers knowledgeable in e-learning and related technologies.
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