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Relevant bibliographies by topics / Measuring learning success

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Academic literature on the topic 'Measuring learning success'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Measuring learning success"

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Kobb, Rita F., Robert J. Lane, and Deidre Stallings. "E-learning and Telehealth: Measuring Your Success." Telemedicine and e-Health 14, no. 6 (August 2008): 576–79. http://dx.doi.org/10.1089/tmj.2007.0103.

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Dean, Kathleen Lis. "Understanding Student Success by Measuring Co-Curricular Learning." New Directions for Institutional Research 2014, no. 164 (November 16, 2015): 27–38. http://dx.doi.org/10.1002/ir.20113.

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Cox, Rebecca D., and Meaghan Dougherty. "(Mis)Measuring Developmental Math Success: Classroom Participants’ Perspectives on Learning." Community College Journal of Research and Practice 43, no. 4 (April 3, 2018): 245–61. http://dx.doi.org/10.1080/10668926.2018.1456378.

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Hassanzadeh, Alireza, Fatemeh Kanaani, and Shában Elahi. "A model for measuring e-learning systems success in universities." Expert Systems with Applications 39, no. 12 (September 2012): 10959–66. http://dx.doi.org/10.1016/j.eswa.2012.03.028.

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McKay II, Donald S., and Timothy J. Ellis. "Measuring Knowledge Enablers and Project Success in IT Organizations." International Journal of Knowledge Management 11, no. 1 (January 2015): 66–83. http://dx.doi.org/10.4018/ijkm.2015010104.

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Knowledge enablers exist at the organizational and project levels. There is however, no meaningful means to measure organizational or project knowledge sharing. The need to understand the elements that enable this flow of knowledge is dramatically evidenced in information technology organizations in which insufficient knowledge sharing leads to intellectual capital loss, rework, skills deterioration, and repeated mistakes that increase project costs or failures. The goal of this study was to examine the relationship among knowledge sharing processes at the organizational level – organizational learning enablers (OLEs) – the project level – project learning enablers (PLEs) – and project success variables (PSVs). After identifying and validating the OLE, PLE, and PSV constructs they were codified in a survey. Results showed a positive and significant relationship among OLEs, PLEs, and PSVs. A multiple regression indicated that the combination of OLEs and PLEs accounted for 30% of a project's success, however, PLEs alone were not statistically significant.

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DOROBAT (SCORTA), Iuliana. "Models for Measuring E-Learning Success in Universities: A Literature Review." Informatica Economica 18, no. 3/2014 (September 30, 2014): 77–90. http://dx.doi.org/10.12948/issn14531305/18.3.2014.07.

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Scalise, Kathleen, Michelle Douskey, and Angelica Stacy. "Measuring learning gains and examining implications for student success in STEM." Higher Education Pedagogies 3, no. 1 (January 2018): 183–95. http://dx.doi.org/10.1080/23752696.2018.1425096.

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Lashayo, Deogratius Mathew. "Measuring E-Learning System Adoption in Universities in Tanzania." International Journal of ICT Research in Africa and the Middle East 9, no. 2 (July 2020): 1–18. http://dx.doi.org/10.4018/ijictrame.2020070101.

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The success of e-learning systems in Tanzania relies on various factors that influence its measurement. Examples of the key factors include trust, environmental factors, and the university readiness. However, influence of these factors towards e-learning systems is not clear. Understanding their impacts and significance helps decision makers and stakeholders in making informed decisions on how to handle them. This study modifies the information systems (IS) success model whereby it adopts 12 factors that had been suggested by this author in his previous study conducted in Open University of Tanzania (OUT) in 2017. A sample of 1,005 students from eight universities in Tanzania was collected. A structural equation modelling was used in data analysis. The results shows trust (T) has positive and significant impact on e-learning actual use (EAU) while environmental factors (EF) had positive and significant impacts on e-learning actual use and perceived benefits, and at the same time, university readiness had a positive and significant impact on perceived benefits (PB).

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Lin, Hsiu-Fen. "Measuring Online Learning Systems Success: Applying the Updated DeLone and McLean Model." CyberPsychology & Behavior 10, no. 6 (December 2007): 817–20. http://dx.doi.org/10.1089/cpb.2007.9948.

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Conklin, Thomas A., and Rama K. Hart. "Appreciative inquiry in management education: measuring the success of co-created learning." Organization Management Journal 6, no. 2 (July 2009): 89–104. http://dx.doi.org/10.1057/omj.2009.14.

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Dissertations / Theses on the topic "Measuring learning success"

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Smith, Dennis R. "Measuring the success of a university \"learning to learn\" course /." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486462702465733.

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Stone, Carmen Mellisa Boatwright. "South Carolina K--12 Online Schools| A Framework for Measuring Success in Teaching and Learning." Thesis, Grand Canyon University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3565380.

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In the United States, K-12-12 online schools are growing, but research on the topic is limited. The purpose of this quantitative, correlational study was to determine if there was a relationship between teacher perceptions of the Effective Schools Correlates and student achievement within two South Carolina online high schools. The independent variable was the Effective Schools correlates, and the dependent variable was the academic achievement of students. The Effective Schools Model provided the theoretical basis for this study because of the documented effectiveness of this model within traditional brick-and-mortar schools. The Effective Schools Survey was used to collect data by measuring South Carolina online high school teacher perceptions of the presence of the Effective Schools correlates. Archival standardized test data of grade 9-12 online students were collected from the South Carolina Department of Education to measure the dependent variables. Findings revealed few statistically significantly relationships between the Effective Schools correlates and student achievement. The findings of this study may interest educational leaders as they seek to identify specific factors or create models to effectively assess teaching and learning within online education.

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Clemmer, Katharine W. "How Involving Secondary Students in the Assessment Process Transforms a Culture of Failure in Mathematics to a Culture of Accountability, Self-Efficacy and Success in Mathematics: Student Action Plans, Assessment, and Cultural Shift." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-79595.

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Learn how to realize a measurable increase in student engagement and achievement in mathematics through a guided, collaborative, and active process grounded in mathematics. Students and teachers collaboratively devise a data-driven plan of action that moves learning forward for all students and effectively supports at-risk secondary students in urban environments. Learn how teachers in the LMU Math and Science Teaching Program effectively implement assessments as motivations for student achievement and develop opportunities for students to demonstrate comprehension and retention of essential content over time. Students become active participants in the assessment process in an environment where learning is an individual progression and risk-taking is valued and encouraged. Find out how students, guided by teacher-provided descriptive feedback, make decisions in a process of self-reflection in which they critically analyze and compare their learning outcomes to expectations of content mastery. By comparing mastery to current performance, students utilize failure and engage in error analysis to deconstruct prior shortcomings and devise a plan of action that will move learning forward thereby overcoming failure.

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Schoeman, Helena. "The Conative Aspects of e-Learning." Diss., 2006. http://hdl.handle.net/2263/26863.

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This study examines how conative factors contribute to effective e-learning for corporate and academic learners. Inference of what effective e-learning activities are were traced during a focus group session, a question on a discussion list, validation of information from different sources such as observers, the verifier, the de-brief session, the scribe, the video and audio recordings and correlation with current literature. Effective learning (self-direction and collaboration), techno- and information literacy and an effective learning environment are the strongest predictors of effective e-learning. The combination of intention (motivation) and action (volition) are the two descriptive factors for conation. The study highlights existing models of conative factors and learner motivation as well as the relationships between conative factors with special reference to internal (pulling) and external (pushing) driving forces. In conclusion, the role of the conative domain in e-learning is seen as being the psychological link between the physical learning environment and effective e-learning.
Dissertation (MEd (Computer-Integrated Education))--University of Pretoria, 2007.
Curriculum Studies
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Books on the topic "Measuring learning success"

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Phillips, Jack J. Measuring for success: What CEOs really think about learning investments. Alexandria, VA: ASTD Press, 2010.

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Phillips, Jack J. Measuring for success: What CEOs really think about learning investments. Alexandria, VA: ASTD Press, 2010.

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Phillips, Jack J. Measuring for success: What CEOs really think about learning investments. Alexandria, VA: ASTD Press, 2010.

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Measuring up to the Ohio learning outcomes and success strategies for the Ohio Proficiency Test: Reading : Level F. Saddle Brook, N.J: Peoples Pub. Group, 2002.

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Measuring up to the Ohio learning outcomes and success strategies for the Ohio Proficiency Test: Writing : Level F : developed specifically for the Ohio Proficiency Test. Saddle Brook, N.J: Peoples Pub. Group, 2001.

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Council, Learning and Skills, ed. Measuring success in the learning and skills sector. Learning and Skills Council, 2003.

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Measuring Up to the Illinois Learning Standards (Success Strategies for the ISAT, Level E). Peoples Publishing Group, Inc., 2002.

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Measuring Up to the New York State Learning Standards and Success Strategies for the State Test (Level B Mathematics). The People's Publishing Company, 2005.

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Measuring Up to the New York State Learning Standards and Success Strategies for the State Test (Science Level D). People's Pub. Gr., 2003.

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Measuring Up to the New York State Learning Standards and Success Strategies for the State Test (English Language Arts, Level F). The People's Publishing Group, Inc., 2003.

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Book chapters on the topic "Measuring learning success"

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Halonen, Jane S., Jennifer L. W. Thompson, Kristin H. Whitlock, R. Eric Landrum, and Sue Frantz. "Measuring meaningful learning in introductory psychology: The IPI student learning outcomes." In Transforming introductory psychology: Expert advice on teacher training, course design, and student success., 57–80. Washington: American Psychological Association, 2022. http://dx.doi.org/10.1037/0000260-004.

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Alsabawy, Ahmed Younis, Aileen Cater-Steel, and Jeffrey Soar. "A Model to Measure E-Learning Systems Success." In Measuring Organizational Information Systems Success, 293–317. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0170-3.ch015.

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E-learning involves adopting and exploiting the potential of new, advanced Information Technology in development and delivery of education. In spite of a rapid growth in the e-learning field there still exists a range of issues facing the stakeholders of e-learning systems. One of the key issues is how to measure e-learning system success. Although considerable attention has been paid to the Information Systems success issue, there remain arguments about the factors which are most effective for measuring Information System success. The issue of measuring Information System success has an impact on evaluating e-learning systems success. This chapter aims to fill this void by proposing an evaluation methodology model to assess e-learning systems success. The contribution of this study is the proposed model to evaluate the success of e-learning systems. The model is based on a thorough review of the e-learning success literature and existing Information Systems success models.

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Martin, Florence, Michele A. Parker, and Abdou Ndoye. "Measuring Success in a Synchronous Virtual Classroom." In Student Satisfaction and Learning Outcomes in E-Learning, 249–66. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-615-2.ch011.

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This chapter will benefit those who teach individuals using the synchronous virtual classroom (SVC). The SVC model will help instructors design online courses that incorporate the factors that students need to be successful. This model will also help virtual classroom instructors and managers develop a systematic way of identifying and addressing the external and internal factors that might impact the success of their instruction. The strategies for empirically researching the SVC, which range from qualitative inquiry to experimental design, are discussed along with practical examples. This information will benefit instructors, researchers, non-profit and profit organizations, and academia.

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Wareing, Shân. "Measuring the Success of Active Blended Learning." In Cases on Active Blended Learning in Higher Education, 291–302. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7856-8.ch015.

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Active blended learning (ABL) is a defining aspect of the University of Northampton and has generated national and global interest. Within a few months of the author taking up a senior leadership position with the university, ABL was a significant positive factor in the university's ability to lock down the campus in response to COVID-19 and deliver education remotely. However, there is a scarcity of evaluation of ABL to provide evidence of the scale of its adoption in the university, its forms in different academic disciplines, its impact on different groups of students, and how to improve its effectiveness. Ideally, evaluation is always integral to pedagogic initiatives. It is however a reality that evaluation comes with challenges. This chapter explores why evaluation is so important and also so difficult. It proposes a way forward in the context of ABL by combining nationally available metrics with small-scale case studies.

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Holsapple, Clyde W., and Anita Lee-Post. "How to Design, Develop, and Deliver Successful E-Learning Initiatives." In Student Satisfaction and Learning Outcomes in E-Learning, 195–229. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-615-2.ch009.

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The purposes of this chapter are three-fold: (1) to present findings in investigating the success factors for designing, developing and delivering e-learning initiatives, (2) to examine the applicability of Information Systems theories to study e-learning success, and (3) to demonstrate the usefulness of action research in furthering understanding of e-learning success. Inspired by issues and challenges experienced in developing an online course, a process approach for measuring and assessing e-learning success is advanced. This approach adopts an Information Systems perspective on e-learning success to address the question of how to guide the design, development, and delivery of successful e-learning initiatives. The validity and applicability of the process approach to measuring and assessing e-learning success is demonstrated in empirical studies involving cycles of action research. Merits of this approach are discussed, and its contributions in paving the way for further research opportunities are presented.

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McKay, Donald Stuart, and Timothy J. Ellis. "Measuring the Relationship Among Learning Enablers and IT Project Success." In Effective Knowledge Management Systems in Modern Society, 212–35. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5427-1.ch011.

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Knowledge enablers exist at the organizational and project levels. There is, however, no meaningful means to measure organizational or project knowledge sharing. The need to understand the elements that enable this flow of knowledge is dramatically evidenced in information technology organizations in which insufficient knowledge sharing leads to intellectual capital loss, rework, skills deterioration, and repeated mistakes that increase project costs or failures. The goal of this chapter is to describe the relationships between knowledge sharing processes at the organizational—organizational learning enablers (OLEs)—and project levels—project learning enablers (PLEs)—with project success variables (PSVs).

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Oliu, Carolina Alejandra. "Theoretical Model of Knowledge Management Capacity to Promote Learning in Organizations." In Comparing High Technology Firms in Developed and Developing Countries, 80–98. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1646-2.ch007.

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Knowledge and its appearance and increasing importance as a new production factor has turned development of technologies, methods, and strategies for its measuring, creation, and diffusion into one of the main priorities achieve sustainable success in any organization. With these ideas, this chapter focuses on offering a model that specifies how a series of procedure mechanisms influence and interact with the learning ability in the organization.

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Guzmán, Jacqueline, Regina Motz, and Alberto Rodrigues da Silva. "Valuing Learning Objects Shared in an Online Community." In Advances in Business Information Systems and Analytics, 529–49. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4373-4.ch028.

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In this chapter, the authors analyze and discuss how the activity inside a social network impacts on the value of a Learning Object (LO) used in a collaborative e-learning platform. Recent works propose metrics for measuring LO reusability based on a variety of approaches. In this work, they combine and extend these approaches in order to design a valuation strategy which helps to identify the usage of LOs inside a social network. Their proposal is to identify the factors that are relevant for the valuation of a LO and determine which of them can be computed automatically from its context of usage, the level of success of its authors and its metadata. The authors’ analysis was performed on a particular social network called LOP (LO Poll) system, which strongly motivates the creation and collaborative valuation of LOs. They present preliminary conclusions obtained from an experiment performed in order to analyze the feasibility of the proposal.

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Harrati, Nouzha, Imed Bouchrika, Zohra Mahfouf, and Ammar Ladjailia. "Evaluation Methods for E-Learning Applications in Terms of User Satisfaction and Interface Usability." In Application Development and Design, 756–77. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3422-8.ch033.

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The use of online technology has become ubiquitous and integral part of our daily life from education to entertainment. Because of the ubiquity of e-learning and vital influence for engineering the educational process, it is no surprise that many research studies are conducted to explore different aspects covering the use of e-learning in higher education. The assessment and evaluation aspects are considered arguably the most influential part for measuring the success and effectiveness of e-learning experience. As more and more universities worldwide have opted to use online technology for their course delivery, research in e-learning systems have attracted considerable interest in order to apprehend how effective and usable e-learning systems in terms of principles related to human computer interaction.

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Harrati, Nouzha, Imed Bouchrika, Zohra Mahfouf, and Ammar Ladjailia. "Evaluation Methods for E-Learning Applications in Terms of User Satisfaction and Interface Usability." In Handbook of Research on Innovative Pedagogies and Technologies for Online Learning in Higher Education, 427–48. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1851-8.ch018.

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The use of online technology has become ubiquitous and integral part of our daily life from education to entertainment. Because of the ubiquity of e-learning and vital influence for engineering the educational process, it is no surprise that many research studies are conducted to explore different aspects covering the use of e-learning in higher education. The assessment and evaluation aspects are considered arguably the most influential part for measuring the success and effectiveness of e-learning experience. As more and more universities worldwide have opted to use online technology for their course delivery, research in e-learning systems have attracted considerable interest in order to apprehend how effective and usable e-learning systems in terms of principles related to human computer interaction.

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Conference papers on the topic "Measuring learning success"

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Shehata, Shady, and Kimberly E. Arnold. "Measuring student success using predictive engine." In LAK '15: the 5th International Learning Analytics and Knowledge Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2723576.2723661.

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Sandjojo, Nidjo, and Tenia Wahyuningrum. "Measuring e-learning systems success: Implementing D & M is success model." In 2015 4th International Conference on Interactive Digital Media (ICIDM). IEEE, 2015. http://dx.doi.org/10.1109/idm.2015.7516343.

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Awang, Hapini, Zahurin Mat Aji, and Wan Rozaini Sheik Osman. "Measuring virtual learning environment success from the teacher’s perspective: Scale development and validation." In PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY (ICAST’18). Author(s), 2018. http://dx.doi.org/10.1063/1.5055430.

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Schimanke, Florian, Robert Mertens, and Bettina Sophie Huck. "Retrieval of Relevant Data for Measuring the Impact of Spaced-Repetition Algorithms on the Learning Success in Mobile Learning Games." In 2019 IEEE International Symposium on Multimedia (ISM). IEEE, 2019. http://dx.doi.org/10.1109/ism46123.2019.00063.

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Booth, Stephanie, Sam C. Berek, Brittoney Green, Nikolai A. Joseph, Emma M. Louden, John Nelson, Claire M. Struebing, Christopher Baker, and Richard Howard. "Measuring and Learning from Success and Innovation in Early Stage Technology Development: A Case Study of NASA’s Center Innovation Fund." In 2018 AIAA SPACE and Astronautics Forum and Exposition. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-5160.

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Armstrong, Andrew G., Christopher A. Mattson, and Randy S. Lewis. "Factors Leading to Sustainable Social Impact on the Affected Communities of Engineering Service Learning Projects." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98407.

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Abstract University engineering programs across the USA engage in service learning projects. These projects involve student teams designing and implementing products or solutions for communities in need, often in developing nations. There has been much research done relating to pedagogy and the impact of these programs on student learning. However, less research has been done on measuring the impact of these programs on the affected communities. This paper examines factors that practitioners believe are related to successfully delivering a desirable and transferable solution to affected communities. The authors identified 46 distinct factors from the literature that implicitly or explicitly are suggested to contribute to successful project outcomes. Formed as postulates in this paper, these 46 factors have been separated into 5 categories to assist understanding and implementing these factors into service learning programs. Lastly, different methods of analyzing and measuring project success and impact are discussed. Future methods for proving the viability of the 46 postulates are discussed as well.

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Astuti, Sri, Zuhrohtun Zuhrohtun, and Kunti Sunaryo. "Evaluation Of Learning Process Based On Outcomes-Based Education (Obe) In Study Program Of Accounting Faculty Of Economics And Business UPN “Veteran” Yogyakarta." In LPPM UPN "VETERAN" Yogyakarta International Conference Series 2020. RSF Press & RESEARCH SYNERGY FOUNDATION, 2020. http://dx.doi.org/10.31098/pss.v1i1.185.

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This study aims to evaluate the success of the learning process in the Accounting Study Program at UPN “Veteran” Yogyakarta. The success of the learning process can be seen from the learning outcomes. This study was conducted in order to prepare an educational curriculum using the OBE approach. This study is survey research conducted on students of the Accounting Study Program at UPN "Veteran" Yogyakarta. The number of respondents is 106. The instrument used in exploring the depth of the survey results is the Learning Outcomes (LO) which will be used in the preparation of the new curriculum “Merdeka Belajar – Kampus Merdeka”. The questionnaire instrument will be used as a basis for measuring the success of the LO which is formulated in the Curriculum Guide for Accounting Study Program of UPN "Veteran" Yogyakarta. LO is the ability to communicate and write; problem-solving skills; ethical reasoning skills; ability to work with teams; the soul of defending the country. Based on the survey results, the following results were obtained: the students' communication and writing skills are still lacking; the ability to solve the problem of the students is still lacking; students' ethical reasoning skills are quite good; the ability to work with the team is still lacking; the spirit of defending the country is still lacking. The results of this survey can be used as an input in developing the MBKM curriculum.

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Lim, Sunghoon, Conrad S. Tucker, Kathryn Jablokow, and Bart Pursel. "Quantifying the Mismatch Between Course Content and Students’ Dialogue in Online Learning Environments." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67339.

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Due to the internet’s increasing global availability, online learning has become a new paradigm for distance learning in higher education. While student interactions and reactions are readily observable in a physical classroom environment, monitoring student interactions and quantifying divergence between lecture topics and the topics that interest students are challenging in online learning platforms. Understanding the effects of this divergence is important for monitoring student engagement and aiding instructors, who are focused on improving the quality of their online courses. The authors of this paper propose a topic modeling method, based on latent Dirichlet allocation (LDA), that quantifies the effects of divergence between course topics (mined from textual transcriptions) and student-discussed topics (mined from discussion forums). Correlations between the measured dissimilarities and (a) the number of posts and comments in discussion forums, (b) the number of submitted assignments, and (c) students’ average performance scores are presented. A case study involving video lecture transcripts and discussion forum posts/comments in a massive open online course (MOOC) platform demonstrates the proposed method’s potential success and informs course providers about the challenges of measuring the topics that interest students.

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Pinxten, Maarten, and Peter Hockicko. "Predicting study success of first-year science and engineering students at the university of Žilina: An exploration of the added value of measuring incoming students' learning and study strategies." In 2016 ELEKTRO. IEEE, 2016. http://dx.doi.org/10.1109/elektro.2016.7512027.

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Ayoobi, Mohsen, Mukasa Ssemakula, and Ana Djuric. "Effectiveness of Evidence-Based Active Learning Pedagogies in Engineering Technology Courses." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87656.

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It is shown in the literature that in the traditional lecture teaching mode, students are mostly passive and do not actively engage in the learning process. Not being engaged in the learning process, students are more likely to lose interest in the material and less likely to comprehend material at a deep level. Consequently, students in traditional lecturing are more prone to simply target meeting the minimum requirements to pass a course. In contrast, active-learning pedagogies have been designed such that students are given the opportunity to engage in the learning process as active participants in the classroom. This promotes better comprehension of the concepts involved. At Wayne State University, statistics on students’ performance indicate that many students entering the Engineering Technology programs either drop or fail to pass courses with a C or better, with success rates being as low as 59% for some courses. To address this issue, the authors have adopted evidence-based active learning techniques in selected courses in the Mechanical Engineering Technology program. The Statics, Dynamics, and Applied Thermodynamics courses were targeted for this initial effort. Statics is a gateway course foundational to the rest of the program, Dynamics is taken right after Statics, and Thermodynamics is one of the most challenging senior level courses. These courses will serve as avenues for measuring the effectiveness (or otherwise) of using active learning techniques in engineering technology education. More specifically, the authors have adopted the following evidence-based techniques: in-class experiments, just-in-time teaching, team quizzes, and students as teachers. This paper describes the specific class activities that were undertaken when implementing the different techniques. The effectiveness of these techniques was measured using students’ persistence in the target courses and the final grades. In addition, standardized concept inventory tests were administered at the beginning and the end of the semester as another measure of the effectiveness of this implementation. Preliminary findings from this study indicate that this project has successfully fostered students’ interest, persistence, and performance.

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Reports on the topic "Measuring learning success"

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Sanz, E., P. Alonso, B. Haidar, H. Ghaemi, and L. García. Key performance indicators (KPIs). Scipedia, 2021. http://dx.doi.org/10.23967/prodphd.2021.9.002.

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The project “Social network tools and procedures for developing entrepreneurial skills in PhD programmes” (prodPhD) aims to implement innovative social network-based methodologies for teaching and learning entrepreneurship in PhD programmes. The multidisciplinary teaching and learning methodologies to be developed will enable entrepreneurship education to be introduced into any PhD programme, providing students with the knowledge, skills, and motivation to engage in entrepreneurial activities. However, the use of the output of the project will depend on the nature and profile of the research or scientific field. In this context, key performance indicators (KPIs) form the base on which the quality and scope of the methodologies developed in the project will be quantified and benchmarked. The project’s final product will be an online tool that higher education students can use to learn entrepreneurship from a social network perspective. Performance measurement is one of the first steps of any project and involves the choice and use of indicators to measure the effectiveness and success of the project’s methods and results. All the KPIs have been selected according to criteria of relevance, measurability, reliability, and adequacy, and they cover the process, dissemination methods, and overall quality of the project. In this document, each KPI is defined together with the units and instruments for measuring it. In the case of qualitative KPIs, five-level Likert scales are defined to improve indicator measurability and reliability. The KPIs for prodPhD are divided into three main dimensions, depending on the stage of the project they evaluate. The three main dimensions are performance and development (which are highly related to the project’s process), dissemination and impact (which are more closely correlated with the project’s output), and overall project quality. Different sources (i.e., European projects and papers) have been drawn upon to define a set of 51 KPIs classified into six categories, according to the project phase they aim to evaluate. An Excel tool has been developed that collects all the KPIs analysed in the production of this document. This tool is shared in the Scipedia repository.

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