13 Apr 2022

373

Educational Data Collection Research Journal Article

Format: APA

Academic level: College

Paper type: Research Paper

Words: 1504

Pages: 5

Downloads: 0

There is more investment in cloud-based Learning Management System (LMS) in Educational institutions, especially when setting up online courses and training programs that enables learners to have access at any given location and time. The right LMS tools can decrease the costs of training by automating the learning process. They also help in updating training materials about regulations set for education programs. It is vital for teachers to choose the right LMS tools for compliance in educational training which will save the institutions from lawsuits and penalties and which will enhance credibility between the education industry and the students. (Kasim & Khalid, 2016). There are several analytics tools each with distinct features which are significant to learners comprising of Blackboard LMS, SkillPort and Learning Analytics tool.

The Blackboard LMS analytic tool is a virtual learning and cloud created controlling tool which is found in self-hosting and managed deployments. The tools help teachers in administering, developing, tracking and conveying digital information to online classes (Alturki & Aldraiweesh, 2016) . It also provides students with access to all courses, gives them opportunities for interacting with their teachers and fellow students and it enables them to upload their work online. Blackboard LMS programs are made up of course handouts, teaching and learning exams (TLE), presentations files and discussion boards.

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Blackboard LMS tool has different features and they are course management, messaging and notifications, course calendar, and progress and tracking (Alturki & Aldraiweesh, 2016). Course management enables educators to add more features like images, slideshows including tables when creating or designing programs for several classes. For example, an instructor may want a program that will help guide the leaners when they have practical classes providing them with instructions and delivering training on what learners prefer. Blackboard also contains course calendars that enable learners to view all the programs that are available and submit registration requests. Another feature of this LMS tool is its certification and training for learners that helps instructors to interact with the learners via video chatting, podcasts, and virtual evaluation tests. Progress tracking and dashboards guide teachers in tracking and viewing the students’ improvement in the program and monitoring class attendance and time spent by the learners. Blackboard also has messaging and notifications tools that provide learners with alerts on deadlines for submission of courses and future events.

Another analytic tool is SkillPort, which is also a cloud-based platform for the management of studies. It helps instructors in tracking, reporting and controlling all the courses in the eLearning curriculum. It also provides curated programs that are in the form of videos, eBooks and certification training. The features of SkillPort include course administration, compliance management, tracking of student records and online assessments (Sarrab, Al-Shihi & Al-Manthari, 2015). Compliance management provides instructors with compliance and regulatory programs that enable learners to gain knowledge on compliance and regulatory issues. Course administration encourages the organization of customized courses of teaching through different locations. Educators can create customized tests for learners with the use of 2D and 3D graphics used in online assessments. Tracking of the students’ progress allows the instructors to view the attendance of students, keep records on their regular attendance and track their performance and scores.

Learning analytics tools have made it easier for technology to estimate the efficiency of educational implementations. Learning analytics tools can take the significant results of the learners, relate them with their training and education data and provide the institutions of learning with dashboard views containing all the information necessary for controlling education successfully. (Conde et al., 2015). The tools can gather inputs from various databases when they are fed essential questions and come up with metrics that can be applied in institutions of learning.

The main features of learning analytics tools are comprised of data sharing capabilities, simple user interface that is scalable and provides reports. One of the essential features considered is the simple user interface which can create a custom reporting user interface that is simpler to use for nontechnical students, but also for students that may be considered expert users. It has data sharing abilities that help educators in preparing reports from the saved analysis which are sent automatically at any given time, to a designated set of learners. It is important for learning analytics coordination to be scalable since the skills can glean large amounts of information and place it into essential materials.

Collection of data is crucial to instructional designers because it helps them in designing the most appropriate experience for learners and study environments (Ritzhaupt, & Kumar, 2015) . The large samples of collected data have incredible capabilities for enhancing the learning methods of students. More research is focusing on creating new models that will predict the performance and success of students and examine their behaviors through online networks. This gives them a better chance of understanding the environments and experiences that are essential for learning. The difference between contexts and courses analytics play a vital role when it comes to predicting whether learners are failing or passing. Therefore, instructional designers are coming up with different models that will make it simpler for learners to comprehend the contextual variables of different programs.

Visualization enables instructional designers to obtain better understanding so they can optimize the learning process by keeping track of students’ interactions. They are also able to create improved programs that fully meet the needs of students.

Visualizations and data collection are significant in enabling learners to comprehend the information and concepts given to them easily. They also enable teachers and educators to capture all the things that students are engaging in when they are online including the creation of algorithms that show students who struggle based on their performances (Bodily & Verbert, 2017). Analytics help instructors to predict the students’ achievements and comprehend their use of diverse resources of learning. Students are also provided with choices that enable them to tailor their experiences of learning. Additionally, reporting helps students in their learning approach based on their behaviors through the delivery of appropriate essential contents of differing learning levels. 

Instructors benefit from the learning analytics as it provides them with effective ways of identifying students who are at risk of failing, dropping out of a course or disengaging from school. Learning analytics also enables instructors to track main pointers like visualizing participation and completed assessments to estimate the risk scores. Instructors are also notified by information from widgets like the Moodle Engagement Analytics Plugin that predicts the students’ progress. This gives educators more opportunities for providing more help and increasing student’ retention.

Learning analytics is significant to institutions and schools as sources of feedback through visualizations and data collection, which help in tweaking courses. This is done through monitoring the student progress, visualizing major issues, exploring data, estimating the early indicators of success and failure, assessing the significance of study material, helping learners who struggle, and improve the methods of teaching (Bodily & Verbert, 2017). Therefore, learning analytics delivers substantial benefits to both students and the organization of learning.

Data collection including other learning analytics is important to CEOs and managers in leading their organizations to success in competitive markets. Managers can reveal insights for making informed choices by visualizing data collected through research. Apart from enabling them to reach competitive edges in the market, it also allows them to identify the main indicators of performance in their various organization departments.

Users also benefit from insights of data collection, visualizations, analytics and reporting which provides them with more knowledge and information. This guides them in setting trends for the peaks of market segments that are important to them. If the users identify more trends, greater purposes will be served in the industries, and they will be able to predict results and conditions across various markets. Therefore, it is important for users to abide by the initial cycle of analytics, data collection, reporting and visualization that will help them find their most essential needs.

Checklists and rubrics are vital tools in stating the ideal criteria of performance, enabling students and teachers to gather information and make judgments concerning their knowledge and outcomes (Suskie, 2018). They offer systematic methods on specific abilities, behaviors, and knowledge hence fitting into the picture of data collection. The significance of information gleaned from checklists and rubrics depend on the involvement of learners directly in comprehending the feedback that is provided. The main purpose of rubrics and checklists is providing students with tools for self-assessment, samples of criteria, systematic recordings for learner’s prior evaluation and data collection.

Rubrics are essential in giving feedback within a shorter period. The two types of rubrics mostly used in learning institutions include self-built rubrics and provided rubrics. The provided rubrics is comprised of already evaluated criteria, definitions, and an already designed scoring strategy. It is usually presented in the form of a table that instructors use when marking assessments and when learners are planning their work. Although the provided rubrics show some positive results for learners, educators still need to design their rubrics. The design of the rubrics can be done in consultation with the students to effectively address their concerns. (Bharuthram & Patel, 2017) . Self-built rubrics help in providing students with some sense of guidance and assists them in accomplishing their targets. At first, students may find it as time-consuming, but the self-assessment outcomes are always exponential. It is vital for educators to inspire learners to develop the self-built rubrics that will help them get a formative self-assessment 

References

Alturki, U. T., & Aldraiweesh, A. (2016). Evaluating the Usability and Accessibility of LMS" Blackboard" at King Saud University.  Contemporary Issues in Education Research 9 (1), 33-44.

Bharuthram, S., & Patel, M. (2017). Co-constructing a rubric checklist with first-year university students: A self-assessment tool.

Bodily, R., & Verbert, K. (2017, March). Trends and issues in student-facing learning analytics reporting systems research. In  Proceedings of the seventh international learning analytics & knowledge conference  (pp. 309-318). ACM.

Conde, M. Á., Hérnandez-García, Á., García-Peñalvo, F. J., & Séin-Echaluce, M. L. (2015, August). Exploring student interactions: Learning analytics tools for student tracking. In  International Conference on Learning and Collaboration Technologies  (pp. 50-61). Springer, Cham.

Kasim, N. N. M., & Khalid, F. (2016). Choosing the right learning management system (LMS) for the higher education institution context: a systematic review.  International Journal of Emerging Technologies in Learning (iJET) 11 (06), 55-61.

Ritzhaupt, A. D., & Kumar, S. (2015). Knowledge and skills needed by instructional designers in higher education.  Performance Improvement Quarterly 28 (3), 51-69.

Sarrab, M., Al-Shihi, H., & Al-Manthari, B. (2015). System Quality Characteristics for Selecting Mobile Learning Applications.  Turkish Online Journal of Distance Education 16 (4), 18-27.

Suskie, L. (2018).  Assessing student learning: A common sense guide . John Wiley & Sons.

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