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ASEE Computers in Education Journal

ASEE's Computers in Education Journal

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Home » learning analytics

learning analytics

An Investigation into Peer-Assisted Learning in an Online Lab Environment

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An Investigation into Peer-Assisted Learning in an Online Lab Environment

Rui Li 1 , John Morelock 2 , Dominik May ​✉ 3 ,

1 Tandon School of Engineering, New York University, B, Brooklyn, New York, 82435, USA
2 Engineering Education Transformations Institute, University of Georgia, Athens, Georgia, USA
3 School of Mechanical Engineering and Safety Engineering University of Wuppertal, Wuppertal Germany

Abstract

Peer learning is one method to encourage meaningful learning in electrical engineering courses. It involves the sharing of ideas, knowledge, and experiences and emphasizes interpersonal learning. However, there are different viewpoints in relation to the best way to implement and assess peer learning in a lab environment, and contemporary literature on online laboratories (OL) rarely explores peer learning opportunities. In this paper, we aim to investigate the benefits of students’ peer learning activity in an online electronics lab course. The key challenge was whether the OL could ensure smooth communication and collaboration between the students.

Read the full article here “An Investigation into Peer-Assisted Learning in an Online Lab Environment”

Learning Analytics of Outcomes-Based Engineering Programs’ Data

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Learning Analytics of Outcomes-Based Engineering Programs’ Data

Anwar Ali Yahya ​✉ 1

1 College of Computer Science and Information Systems, Najran University, Saudi Arabia
2 Faculty of Computer Science and Information Systems, Thamar University, Thamar, Yemen

Abstract

In the recent years, learning analytics is attracting attention in tertiary education sector. This paper presents a case study of applying learning analytics approaches to discover knowledge from Outcome Based (OB) engineering programs’ data. More specifically, Association Rule Mining approach is applied to a dataset extracted from the Self-Study Reports of 152 engineering programs accredited by American Board of Engineering and Technology (ABET). In doing so, the dataset has been processed and transformed into a suitable representation. Apriori algorithm is then applied to generate rules involving PEOs and ABET SOs. The generated rules are filtered, and the filtered rules are used to draw a set of generic rules for mapping each PEO to ABET SOs and to discover the correlations among ABET SOs.

Read the full article here “Learning Analytics of Outcomes-Based Engineering Programs’ Data”

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Recent Articles

  • On Building and Implementing Adaptive Learning Platform Lessons for Pre-Class Learning in a Flipped Course
  • PSpice Model of a Shunt DC Motor for Transient Performance Simulation and Its Use in Teaching
  • Collaborative Senior Design Capstone at Two Geographically Separated Universities
  • Enhancing Computer Science Education with Pair Programming and Problem Solving Studios
  • Mitigating Engineering Student Attrition by Implementing Arduino Activities Throughout Undergraduate Curricula
  • Active Learning Undergraduate Course on UAV Path Planning and Tracking Using Numerical Simulation

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