Creating Student Interaction Profiles for Adaptive Collaboration Gamification Design

2016 ◽  
Vol 7 (3) ◽  
pp. 47-62 ◽  
Author(s):  
Antti Knutas ◽  
Jouni Ikonen ◽  
Dario Maggiorini ◽  
Laura Ripamonti ◽  
Jari Porras
Keyword(s):  
Online Learning ◽  
Collaborative Learning ◽  
Learning Environments ◽  
Interaction Analysis ◽  
Student Interaction ◽  
Evidence Based ◽  
Educational Settings ◽  
Online Learning Environments ◽  
Game Elements

Benefits of collaborative learning are established and gamification methods have been used to motivate students towards achieving course goals in educational settings. However, different users prefer different game elements and rewarding approaches and static gamification approaches can be inefficient. The authors present an evidence-based method and a case study where interaction analysis and k-means clustering are used to create gamification preference profiles. These profiles can be used to create adaptive gamification approaches for online learning or collaborative learning environments, improving on static gamification designs. Furthermore, the authors discuss possibilities for using our approach in collaborative online learning environments.

An Ecological Model of Student Interaction in Online Learning Environments

2020 ◽  
pp. 387-415
Author(s):  
Genevieve Marie Johnson ◽  
Audrey Cooke
Keyword(s):  
Online Learning ◽  
Instructional Design ◽  
Learning Environments ◽  
Ecological Model ◽  
Ecological Theory ◽  
Ecological Systems ◽  
Student Interaction ◽  
Online Learning Environments ◽  
University Culture ◽  
Environmental Systems

Ecological theory conceptualized the student as surrounded by a series of environmental systems and the processes of learning as interaction between the student (i.e., bioecology) and the systems (i.e., microsystem, exosystem and macrosystem). This chapter synthesizes the literature and proposes an ecological model of student interaction in online learning environments. Specifically, learner-learner, learner-instructor and learner-content interactions occur in the microsystem and are mediated by the interface subsystem. Student microsystemic interactions influence and are influenced by the instructional design exosystem. The macrosystem reflects the indirect influence of university culture on all aspects of the microsystem, exosystem and interface subsystem. The chronosystem captures the effect of time on the student and on all ecological systems (e.g., students mature and university culture evolves)

scholarly journals The influence of online mathematics learning on prospective teachers mathematics achievement: The role of independent and collaborative learning

2019 ◽  
Vol 11 (4) ◽  
pp. 257-265
Author(s):  
Murat Tezer ◽  
Ezgi P. Yildiz ◽  
Seyma Bozkurt ◽  
Hasan Tangul
Keyword(s):  
Online Learning ◽  
Collaborative Learning ◽  
Mathematics Achievement ◽  
Learning Environments ◽  
Prospective Teachers ◽  
Mathematics Learning ◽  
Online Learning Environments ◽  
Post Test ◽  
Online Mathematics

The aim of this study is to influence of online mathematics learning on prospective teachers mathematics achievement based on the role of independent and collaborative learning. An experimental design model with pre-test and post-test control group was used in the study. The working group constitutes a total of 60 prospective teachers in the first and second years of education in the Department of Elementary Teaching and Preschool Teaching of a private university in 2016–2017 academic year in Northern Cyprus. As a means of data collection, mathematics achievement test consisting of 30 questions was administered as pre-test, and after the study, the same success test was administered as a post-test. As a result of the findings, it has been determined that the prospective teachers have a significant increase in their successes due to the teaching practices in online learning environments. Keywords: Online learning environments, independent learning, Moodle, mathematics achievement, teacher candidate, intelligence.

Case Study Research and Online Learning

2011 ◽  
pp. 70-80 ◽  
Author(s):  
Bruce L. Mann
Keyword(s):  
Online Learning ◽  
Learning Environments ◽  
Research Methodology ◽  
Teaching And Learning ◽  
Case Study Research ◽  
Online Learning Environments ◽  
Study Research

As a research methodology, case study is very popular among researchers doing investigations of Internet-supported teaching and learning. This chapter will discuss considerations for conducting case study research in online and blended (on-site and online) learning environments.

scholarly journals The Impact of Students’ Perceptions of Online Learning Environments on Students’ Satisfaction in the Context of Covid-19 Pandemic

2021 ◽  
Vol 2 (3) ◽  
pp. 61-72
Author(s):  
Sunipa Ghosh Dastidar
Keyword(s):  
Online Learning ◽  
Active Learning ◽  
Student Satisfaction ◽  
Learning Environments ◽  
Student Interaction ◽  
Online Learning Environments ◽  
Student Autonomy ◽  
Instructor Support ◽  
The Impact ◽  
Students Satisfaction

In the context of the Covid-19 pandemic, the present study aimed to examine students’ perceptions of online learning environments and students’ satisfaction based on their academic stream. The study also investigated the impact of students’ perceptions of online learning environments on students’ satisfaction. A quantitative descriptive survey method was applied. This study included 230 students (130 undergraduate and 100 postgraduate students) from colleges and universities of West Bengal. Online Learning Environments Survey, an adapted and translated (Bengali) version of the Distance Education Learning Environments Survey (DELES) by Scott L Walker (2003), was used for collecting data. For data analysis, statistical techniques, ANOVA and regression analysis were performed. The results revealed significant mean differences among arts, commerce, and science students’ perceptions of online learning environments in the dimensions of student interaction and collaboration, personal relevance, authentic learning, active learning, and student autonomy except in instructor support. Furthermore, a significant mean difference in student satisfaction was found based on the academic stream. The result revealed that overall students’ perceptions of online learning environments had a significant impact on student satisfaction, with student interaction and collaboration being the most significant predictor of all; however, instructor support, active learning, and student autonomy were not found to be significant predictors of student satisfaction.

scholarly journals Microlearning and computer-supported collaborative learning: An agenda towards a comprehensive online learning system

2021 ◽  
Vol 1 (4) ◽  
pp. 225
Author(s):  
Soheila Garshasbi ◽  
Brian Yecies ◽  
Jun Shen
Keyword(s):  
Online Learning ◽  
Collaborative Learning ◽  
Learning Environments ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Smart Devices ◽  
Computer Supported Collaborative Learning ◽  
Online Learning Environments ◽  
Learning Platform ◽  
Intelligent Tools

<p style='text-indent:20px;'>With the rise of the COVID-19 pandemic and its inevitable consequences in education, increased demand for robust online learning frameworks has occurred at all levels of the education system. Given the transformative power of Artificial Intelligence (AI) and machine learning algorithms, there have been determined attempts through the design and application of intelligent tools to overcome existing challenges in online learning platforms. Accordingly, educational providers and researchers are investigating and developing intelligent online learning environments which share greater commonalities with real-world classroom conditions in order to better meet learners' needs. However, short attention spans and the widespread use of smart devices and social media bring about new e-learning systems known as microlearning (ML). While there has been ample research investigating ML and developing micro-content, pedagogical challenges and a general lack of alternative frameworks, theories and practices still exist. The present models have little to say about the connections between social interaction, including learner–content, learner–instructor and learner–learner communication. This has prompted us to investigate the complementary aspects of Computer-supported Collaborative Learning (CSCL) as an interactive learning model, along with an embedded ML module in the design and development of a comprehensive learning platform. The purpose of this study is to explore the pedagogical frameworks and challenges with reference to interaction and retention in online learning environments, as well as the theoretical and pedagogical foundations of ML and its applications. In addition, we delve into the theories and principles behind CSCL, the main elements in CSCL, identifying the issues and challenges to be faced in improving the efficacy of collaboration processes and outcomes. In short, we aim to synthesize how microlearning and CSCL can be applied as effective modules within a comprehensive online learning platform, thereby offering STEM educators a relevant roadmap towards progress that has yet to be offered in previous studies.</p>

Using students' perceptions of participation in collaborative learning activities in the design of online learning environments

2001 ◽  
Vol 19 (2) ◽  
pp. 83-106 ◽  
Author(s):  
Georgia Zafeiriou ◽  
José Miguel Baptista Nunes ◽  
Nigel Ford
Keyword(s):  
Online Learning ◽  
Collaborative Learning ◽  
Learning Environments ◽  
Learning Activities ◽  
Online Learning Environments
Sign in / Sign up

Export Citation Format

Share Document