Welcome to Realizeit innovation technology Review
Different Authors reviews the Realizeit innovative technology.
The paper, Learning and Academic Analytics in the Realizeit System (2014), focuses on how analytics are integrated into the Realizeit adaptive learning platform to support both learning and institutional decision-making.
Separation of Curriculum from Content: Realizeit separates curriculum from content to reduce cognitive load on learners by making data-driven decisions that guide them through their learning path. The platform supports curriculum creation, content delivery, and data capture.
Learning Analytics: The platform continuously tracks learner progress, identifying strengths, weaknesses, and preferences. Real-time data provides insights into learners' abilities and informs instructors of potential intervention points.
Academic Analytics: At the institutional level, Realizeit provides a comprehensive view of learner and instructor data. This allows for analysis of course performance, faculty resource planning, and the identification of trends, such as course relationships and instructor impact.
Competency-Based Learning: Learners progress based on mastery rather than time, making the platform flexible and personalized.
Data Visualization: Both learners and instructors benefit from dashboards that visualize real-time progress, engagement, and outcomes. These visual tools provide actionable insights, making data more accessible and easier to interpret.
Course Analytical Reviews: These automated reviews provide institutions with a one-click analysis of data related to course structure, student numbers, engagement, outcomes, and more. This allows for early detection of trends, correlations, and performance indicators.
Realizeit’s use of granular data, real-time learning analytics, and institutional-level academic analytics offers significant potential for improving educational outcomes. It allows for personalized learning experiences, supports instructor interventions, and helps institutions make data-driven decisions. The paper highlights how analytics in adaptive learning platforms like Realizeit can transform the educational process by making data actionable.
This paper can be an excellent resource if you're looking into adaptive learning systems or learning analytics within higher education contexts. It gives a detailed overview of how data can inform both personalized learning paths and larger institutional strategies(LearningandAcademicAnal…).
The paper Real World Usage of an Adaptive Testing Algorithm to Uncover Latent Knowledge by Danny Lynch and Colm P. Howlin explores the Determine Knowledge (DK) algorithm, which is used in the Realizeit adaptive learning platform to identify students' pre-existing or "latent" knowledge at the beginning of a course. The paper emphasizes the importance of uncovering this latent knowledge to improve learning efficiency, allowing students to skip content they already know and focus on new material. The algorithm operates by testing students on key curriculum nodes, updating their learning path based on correct or incorrect responses, thus identifying what they already know.
The DK algorithm is compared with the Knowledge Space Theory (KST) algorithm in terms of efficiency and accuracy. Simulated studies show that the DK algorithm generally asks fewer questions but may sacrifice accuracy in some cases, depending on the size of a student's latent knowledge state and their response rate. The study also reviews real-world usage data from a higher education institution in the U.S., involving over 455,000 instances of the DK algorithm used by more than 48,000 students across various subjects. The data demonstrates that the DK algorithm is particularly efficient for students with very little or a lot of prior knowledge but less efficient for those in between.
The paper concludes with a discussion on improvements to the algorithm based on real-world findings. These include using a slider to help students self-assess their prior knowledge before starting the DK process and employing probabilistic methods to account for errors in student responses. Overall, the study highlights the effectiveness of the DK algorithm in enhancing learning efficiency by personalizing the starting point for each student based on their existing knowledge.
Reference
Howlin, C., & Lynch, D. (2014, October). Learning and academic analytics in the realizeit system. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 862-872). Association for the Advancement of Computing in Education (AACE).
Lynch, D., & Howlin, C. (2014). Real world usage of an adaptive testing algorithm to uncover latent knowledge. In ICERI2014 Proceedings (pp. 504-511). IATED.