Mining student data to assess the impact of moodle activities and prior knowledge on programming course success

2015 ◽  
Author(s):  
Sabina Sisovic ◽  
Maja Matetic ◽  
Marija Brkic Bakaric
Keyword(s):  
Prior Knowledge ◽  
Course Success ◽  
Student Data ◽  
The Impact

A noise reduction method for semi-supervised community detection based on harmonic function

2018 ◽  
Vol 32 (14) ◽  
pp. 1850166 ◽  
Author(s):  
Lilin Fan ◽  
Kaiyuan Song ◽  
Dong Liu
Keyword(s):  
Harmonic Function ◽  
Prior Knowledge ◽  
Community Detection ◽  
Detection Methods ◽  
Detection Process ◽  
Knowledge Based ◽  
Detection Approach ◽  
Series Of Experiments ◽  
Novel Strategy ◽  
The Impact

Semi-supervised community detection is an important research topic in the field of complex network, which incorporates prior knowledge and topology to guide the community detection process. However, most of the previous work ignores the impact of the noise from prior knowledge during the community detection process. This paper proposes a novel strategy to identify and remove the noise from prior knowledge based on harmonic function, so as to make use of prior knowledge more efficiently. Finally, this strategy is applied to three state-of-the-art semi-supervised community detection methods. A series of experiments on both real and artificial networks demonstrate that the accuracy of semi-supervised community detection approach can be further improved.

In search of virtuous learning circles: absorptive capacity and its antecedents in the education sector

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Regina Lenart-Gansiniec ◽  
Wojciech Czakon ◽  
Massimiliano Matteo Pellegrini
Keyword(s):  
Organizational Learning ◽  
Prior Knowledge ◽  
Absorptive Capacity ◽  
Structural Equation ◽  
Knowledge Flows ◽  
Measurement Scale ◽  
Structured Interviews ◽  
Measurement Scales ◽  
Content Type ◽  
The Impact

Purpose This study aims to identify context-specific antecedents to schools’ absorptive capacity (AC) and to show how those can enact “a virtuous learning circle.” Design/methodology/approach The study uses a mixed method: an exploration based on semi-structured interviews with educational experts; the development of a measurement scale and a partial least squares structural equation modelling to test the impact of the antecedents. Findings The results yielded four empirically-grounded antecedents and their measurement scales, namely, prior knowledge, employees’ skills, educational projects and interactions with the environment (Studies one and two). All antecedents are significantly and positively related to AC processes (study three). Using the organizational learning theory perspective, the results have been interpreted as an AC “virtuous learning circle.” Practical implications With increasing pressures to adapt, a case of which was the COVID-19 pandemic, schools can greatly benefit from absorbing knowledge flows. This suggests the construction a favourable environment for AC. To this end, the individual (employees’ prior knowledge and skills), organizational (educational projects) and institutional level of managerial action (interactions with the environment) can be effective when create a recursive organizational learning circle. In addition, this study offers an expert-validated measurement scale for self-assessment of a school’s specific contingencies, and thus, for planning of punctual interventions to develop AC. Originality/value This study advances the existing body of knowledge management in the educational context by rigorously identifying and validating a scale for measuring the antecedents of AC and developing an interpretive approach to the AC “virtuous circle.”

scholarly journals A Pronunciation Prior Assisted Vowel Reduction Detection Framework with Multi-Stream Attention Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8321
Author(s):  
Zongming Liu ◽  
Zhihua Huang ◽  
Li Wang ◽  
Pengyuan Zhang
Keyword(s):  
Prior Knowledge ◽  
Language Learners ◽  
Native Speakers ◽  
Recognition Rate ◽  
Auxiliary Information ◽  
Vowel Reduction ◽  
Phoneme Recognition ◽  
End To End ◽  
The Impact ◽  
Auxiliary Input

Vowel reduction is a common pronunciation phenomenon in stress-timed languages like English. Native speakers tend to weaken unstressed vowels into a schwa-like sound. It is an essential factor that makes the accent of language learners sound unnatural. To improve vowel reduction detection in a phoneme recognition framework, we propose an end-to-end vowel reduction detection method that introduces pronunciation prior knowledge as auxiliary information. In particular, we have designed two methods for automatically generating pronunciation prior sequences from reference texts and have implemented a main and auxiliary encoder structure that uses hierarchical attention mechanisms to utilize the pronunciation prior information and acoustic information dynamically. In addition, we also propose a method to realize the feature enhancement after encoding by using the attention mechanism between different streams to obtain expanded multi-streams. Compared with the HMM-DNN hybrid method and the general end-to-end method, the average F1 score of our approach for the two types of vowel reduction detection increased by 8.8% and 6.9%, respectively. The overall phoneme recognition rate increased by 5.8% and 5.0%, respectively. The experimental part further analyzes why the pronunciation prior knowledge auxiliary input is effective and the impact of different pronunciation prior knowledge types on performance.

scholarly journals The Impact of Prior Knowledge about Visual Feedback on Motor Performance and Learning

2013 ◽  
Vol 03 (01) ◽  
pp. 1-9 ◽  
Author(s):  
James J. Burkitt ◽  
Lawrence E. M. Grierson ◽  
Victoria Staite ◽  
Digby Elliott ◽  
James Lyons
Keyword(s):  
Prior Knowledge ◽  
Visual Feedback ◽  
Motor Performance ◽  
The Impact

Encouraging and Increasing Student Engagement and Participation in an Online Classroom

2016 ◽  
pp. 426-447
Author(s):  
Kathryn Woods
Keyword(s):  
Online Learning ◽  
Student Engagement ◽  
Learning Environment ◽  
Prior Knowledge ◽  
Learning Environments ◽  
Online Courses ◽  
Traditional Classroom ◽  
Online Classroom ◽  
Online Learning Environments ◽  
The Impact

Advances in technology have increased opportunities for students to participate in online courses. While some instructors are beginning their careers teaching only online courses, others are discovering a need to teach sections of courses online after they have enjoyed a long career teaching in a traditional classroom. In either situation, it is important for instructors to recognize that students in online learning environments require the use of different strategies for encouraging engagement and participation in class. In this chapter, the author describes the challenges that students and instructors face specifically in the online learning environment as well as strategies for success, including how to maximize the impact of students' experiences and prior knowledge, using multiple platforms to deliver information, discouraging procrastination, setting clear expectations, encouraging individuality, capitalizing on diversity, and providing and utilizing helpful resources.

scholarly journals Student Vulnerability, Agency and Learning Analytics: An Exploration

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Paul Prinsloo ◽  
Sharon Slade
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Approaches To Learning ◽  
Fiduciary Duty ◽  
Power Relationship ◽  
Opting Out ◽  
Student Data ◽  
Individual Vulnerability ◽  
Regulatory Frameworks ◽  
The Impact

The increasing potential and practice of collecting, analysing and using student data necessitates that higher education institutions (HEIs) critically examine their assumptions, paradigms and practices regarding student data. There is a real danger that some current approaches to learning analytics within higher education ignore the fiduciary duty of HEIs and the impact and scope of the asymmetrical power relationship between students and the institution. In the light of increasing concerns regarding surveillance, higher education cannot afford a simple paternalistic approach to the use of student data. Very few HEIs have regulatory frameworks in place and/or share information with students regarding the scope of data that may be collected, analysed, used and shared. It is clear from literature that basic opting in or opting out does not sufficiently allow for many of the complex issues in the nexus of privacy, consent, vulnerability and agency. The notion of vulnerability (institutional and individual) allows an interesting and useful lens on the collection and use of student data. Though both institutional and individual vulnerability needs to be considered, this paper focuses specifically on student vulnerability. An earlier framework developed by Prinsloo and Slade provides tentative pointers to consider a range of responses to decrease students’ vulnerability, increase students’ agency and move students as participants in learning analytics from quantified selves to qualified selves.

Sign in / Sign up

Export Citation Format

Share Document