Smart Learning Analytics and Frequent Formative Assessments to Improve Student Retention

2018 ◽  
Author(s):  
Muhammad Mustafa Hassan ◽  
Adnan N. Qureshi ◽  
Andres Moreno ◽  
Markku Tukiainen
Keyword(s):  
Student Retention ◽  
Learning Analytics ◽  
Formative Assessments ◽  
Smart Learning

scholarly journals Widening the net to reduce the debt: Reducing student debt by increasing identification of completely disengaged students

2020 ◽  
Author(s):  
Neil Van Der Ploeg ◽  
Kelly Linden ◽  
Ben Hicks ◽  
Prue Gonzalez
Keyword(s):  
Higher Education ◽  
Student Retention ◽  
Predictive Model ◽  
Hybrid Model ◽  
Learning Analytics ◽  
Teaching Staff ◽  
Student Debt ◽  
Education Standards ◽  
Performance Based Funding ◽  
Panel Review

Student Retention and Attrition guidelines are part of the Federal Government’s performance based funding framework. One of the recommendations from the Higher Education Standards Panel review is to consider changing students’ enrolment prior to census date when a certain level of engagement is not met. This study investigates this recommendation by trialing and testing a model to see if completely disengaged students are able to be retrospectively identified as at risk of failing all subjects. Using learning analytics alone to create a predictive model at scale proved to be very difficult. When applied to session 1 of 2019, even the strictest criteria included five false positives out of 17 identified students. There is promise, however, that a hybrid model of learning analytics with additional oversight from teaching staff could be a solution, but this needs further research.

scholarly journals Learning Analytics to Inform the Learning Design: Supporting Instructor’s Inquiry into Student Learning in Unsupervised Technology-Enhanced Platforms

2019 ◽  
Vol 23 (3) ◽  
Author(s):  
Priya Harindranathan ◽  
James Folkestad
Keyword(s):  
Student Learning ◽  
Learning Strategies ◽  
Learning Analytics ◽  
Formative Assessments ◽  
Technology Enhanced Learning ◽  
Learning Tools ◽  
Learning Behaviors ◽  
Pedagogical Design ◽  
Actual Use ◽  
Learning Platforms

Instructors may design and implement formative assessments on technology-enhanced platforms (e.g., online quizzes) with the intention of encouraging the use of effective learning strategies like active retrieval of information and spaced practice among their students. However, when students interact with unsupervised technology-enhanced learning platforms, instructors are often unaware of students’ actual use of the learning tools with respect to the pedagogical design. In this study, we designed and extracted five variables from the Canvas quiz-log data, which can provide insights into students’ learning behaviors. Anchoring our conceptual basis on the ‘influential conversational framework’, we find that learning analytics (LA) can provide instructors with critical information related to students’ learning behaviors, thereby supporting instructors’ inquiry into student learning in unsupervised technology-enhanced platforms. Our findings suggest that the information that LA provides may enable instructors to provide meaningful feedback to learners and improve the existing learning designs.

scholarly journals Mining Smart Learning Analytics Data Using Ensemble Classifiers

2020 ◽  
Vol 15 (12) ◽  
pp. 81
Author(s):  
Samina Kausar ◽  
Solomon Sunday Oyelere ◽  
Yass Khudheir Salal ◽  
Sadiq Hussain ◽  
Mehmet Akif Cifci ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Recent Progress ◽  
Educational Systems ◽  
Context Aware ◽  
Ensemble Classifiers ◽  
Ubiquitous Learning ◽  
Web Based ◽  
Smart Learning ◽  
Ensemble Techniques ◽  
Intelligent Tools

Recent progress in technology has altered the learning behaviors of students; besides giving a new impulse which reshapes the education itself. It can easily be said that the improvements in technologies empower students to learn more efficiently, effectively and contentedly. Smart Learning (SL) despite not being a new concept describing learning methods in the digital age- has caught attention of researchers. Smart Learning Analytics (SLA) provides students of all ages with research-proven frameworks, helping students to benefit from all kinds of resources and intelligent tools. It aims to stimulate students to have a deep comprehension of the context and leads to higher levels of achievements. The transformation of education to smart learning will be realized by reengineering the fundamental structures and operations of conventional educational systems. Accordingly, students can learn the proper information yet to support to learn real-world context, more and more factors are needed to be taken into account. Learning has shifted from web-based dumb materials to context-aware smart ubiquitous learning. In the study, a SLA dataset was explored and advanced ensemble techniques were applied for the classification task. Bagging Tree and Stacking Classifiers have outperformed other classical techniques with an accuracy of 79% and 78% respectively.

scholarly journals The PERLA Framework: Blending Personalization and Learning Analytics

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Mohamed Amine Chatti ◽  
Arham Muslim
Keyword(s):  
Learning Environments ◽  
Learning Analytics ◽  
Theoretical Foundation ◽  
Personalized Learning ◽  
Systematic Design ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Learning Contexts ◽  
Customized Learning ◽  
Opening Up

Personalization is crucial for achieving smart learning environments in different lifelong learning contexts. There is a need to shift from one-size-fits-all systems to personalized learning environments that give control to the learners. Recently, learning analytics (LA) is opening up new opportunities for promoting personalization by providing insights and understanding into how learners learn and supporting customized learning experiences that meet their goals and needs. This paper discusses the Personalization and Learning Analytics (PERLA) framework which represents the convergence of personalization and learning analytics and provides a theoretical foundation for effective analytics-enhanced personalized learning. The main aim of the PERLA framework is to guide the systematic design and development of effective indicators for personalized learning.

Smart Learning Analytics: Conceptual Modeling and Agile Engineering

2018 ◽  
pp. 3-16 ◽  
Author(s):  
Vladimir L. Uskov ◽  
Jeffrey P. Bakken ◽  
Ashok Shah ◽  
Timothy Krock ◽  
Alexander Uskov ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Conceptual Modeling ◽  
Smart Learning

A Study on the Impact of Learning Analytics to Evaluate Students' Performance in Higher Education

2018 ◽  
pp. 19-27
Author(s):  
Neerja Singh
Keyword(s):  
Higher Education ◽  
Student Retention ◽  
Learning Analytics ◽  
Educational Institutions ◽  
Massive Scale ◽  
Overall Performance ◽  
The Impact ◽  
The Way

Learning analytics is receiving increased awareness because it helps educational institutions in growing student retention, enhancing student fulfillment, and easing the burden of accountability. Although those massive-scale issues are worthy of attention, schools may additionally be inquisitive about how they can use learning analytics in their personal guides to assist their students. In this chapter, the authors define learning analytics, the way it has been used in educational establishments, what learning analytics tools are available, and how college can make use of facts in their publications to reveal scholar overall performance. Finally, the authors articulate several problems and uncertainties with the usage of learning analytics in higher education.

Learning Analytics: The Emperor’s New Clothes?

2017 ◽  
Vol 3 (1) ◽  
pp. 76
Author(s):  
Sue Milward
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Student Retention ◽  
Student Learning ◽  
Learning Analytics

Learning Analytics is promising to deliver the power of big data to Higher Education. By extracting meaning from the myriad of data held against a student, Learning Analytics promises to improve student retention and attainment. However, there are challenges to be overcome before the reality can live up to the promises.  

Sign in / Sign up

Export Citation Format

Share Document