Evaluation of Web Based Behavioral Interventions Using Spoken Tutorials

Eranki, Kiran L. N.; Moudgalya, Kannan M.

doi:10.1109/t4e.2012.12

Cited by 17 publications

(7 citation statements)

References 13 publications

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“…In brief, these findings suggest that normalization can have a negative effect on the performance of the spectral algorithm in datasets with 15 or fewer features; however, it appears to mostly have a positive impact on several other clustering methods. This result is in line with those reported by other researchers, e.g., [30], where they state that spectral clustering, in general, appears to be sensitive to different measures. On the other hand, methods such as OPTICS and agglomerative clustering tend to benefit more from normalization in datasets with a smaller number of features (15 or smaller), whereas kmedoids clustering benefits more from normalization regarding datasets with more than 15 features.…”

Section: A Effect Of Normalization On the Performance Of Clustering supporting

confidence: 93%

“…A significant number of EDM and LA studies have applied clustering methods to educational data to provide instructors and students with useful insights into their learning processes. For example, in a study conducted by [29] and [30], different clustering methods, including k-means, were employed to cluster the e-learning behavior of students and determine the impact of human characteristics on students preferences, respectively. Similarly, using item response theory and kmeans [31] could successfully identify the learning ability of students in a collaborative-learning environment.…”

Section: Related Workmentioning

confidence: 99%

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Clustering Algorithms in an Educational Context: An Automatic Comparative Approach

et al. 2020

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Despite an increasing consensus regarding the significance of properly identifying the most suitable clustering method for a given problem, a surprising amount of educational research, including both educational data mining (EDM) and learning analytics (LA), neglects this critical task. This shortcoming could in many cases have a negative impact on the prediction power of both the EDM and LA based approaches. To address such issues, this work proposes an evaluation approach that automatically compares several clustering methods using multiple internal and external performance measures on 9 real-world educational datasets of different sizes, created from the University of Tartu's Moodle system, to produce twoway clustering. Moreover, to investigate the possible effect of normalization on the performance of the clustering algorithms, this work performs the same experiment on a normalized version of the datasets. Since such an exhaustive evaluation includes multiple criteria, the proposed approach employs a multiple criteria decision-making method (i.e., TOPSIS) to rank the most suitable methods for each dataset. Our results reveal that the proposed approach can automatically compare the performance of the clustering methods and accordingly recommend the most suitable method for each dataset. Furthermore, our results show that in both normalized and nonnormalized datasets of different sizes with 10 features, DBSCAN and k-medoids are the best clustering methods, whereas agglomerative and spectral methods appear to be among the most stable and highly performing clustering methods for such datasets with 15 features. Regarding datasets with more than 15 features, OPTICS is among the top-ranked algorithms among the nonnormalized datasets, and kmedoids is the best among the normalized datasets. Interestingly, our findings reveal that normalization may have a negative effect on the performance of certain methods, e.g., spectral clustering and OPTICS; however, it appears to mostly have a positive impact on all of the other clustering methods.

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Section: A Effect Of Normalization On the Performance Of Clustering supporting

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Clustering Algorithms in an Educational Context: An Automatic Comparative Approach

et al. 2020

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“…In the case of screencast technologies, this indicator is used to understand how the learners perceive and understand the tutorials. Eranki and Moudgalya (2012) presented a study on the relationship between behavioural interventions and user preferences while using screencast technologies. Participants were divided into novice and experts based on a pre-test questionnaire and their Web-basede-learning preferences were identified using the systematic usability scale questionnaire.…”

Section: Spoken Tutorialmentioning

confidence: 99%

Digital Literacy Key Performance Indicators for Sustainable Development

Radovanović¹,

Holst

Belur

et al. 2020

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The concept of digital literacy has been defined in numerous ways over the last two decades to incorporate rapid technological changes, its versatility, and to bridge the global digital divide. Most approaches have been technology-centric with an inherent assumption of cultural and political neutrality of new media technologies. There are multiple hurdles in every stage of digital literacy implementation. The lack of solutions such as local language digital interfaces, locally relevant content, digital literacy training, the use of icons and audio excludes a large fraction of illiterate people. In this article, we analyse case studies targeted at under-connected people in sub-Saharan Africa and India that use digital literacy programmes to build knowledge and health literacy, solve societal problems and foster development. In India, we focus on notable initiatives undertaken in the domain of digital literacy for rural populations. In Sub-Saharan Africa, we draw from an original project in Kenya aiming at developing digital literacy for youth from low-income backgrounds. We further focus on Senegal, Mali, Burkina Faso and Tanzania, where field studies have been conducted on the use of digital technologies by low-literacy people and on how audio and icon-based interfaces and Internet lite standard could help them overcome their limitations. The main objective of this article is to identify key performance indicators (KPIs) in the context of digital literacy skills as one of the pillars for digital inclusion. We will learn how digital literacy programmes can be used to build digital literacy and how KPIs for sustainable development can be established. In the final discussion, we offer lessons learned from the case studies and further recommendation for stakeholders and decision-makers in the field of digital health literacy.

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“…The students also rate the videos. This effort is supported by an online portal, designed with student feedback [3]. Suitability for self study and the online portal have made this effort extremely popular, resulting in 200 to 300 workshops every month, training about 30 students in each workshop.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Student Perceptions and Interests Using Spoken Tutorials in Online Courses

Eranki

Moudgalya

2012

2012 IEEE 12th International Conference on Advanced Learning Technologies

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We evaluate the perceptions and the intensity of students who study online courses using video tutorials. Using the data of the past viewers, and the concept of similarity of interest, we recommend videos for further learning. As the total number of videos in the repertoire is large, this helps reduce the information overload. Although spoken tutorials are used for this study, the results are generic. This work is based on an empirical model obtained through a data mining approach.

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Evaluation of Web Based Behavioral Interventions Using Spoken Tutorials

Cited by 17 publications

References 13 publications

Clustering Algorithms in an Educational Context: An Automatic Comparative Approach

Clustering Algorithms in an Educational Context: An Automatic Comparative Approach

Digital Literacy Key Performance Indicators for Sustainable Development

Evaluation of Student Perceptions and Interests Using Spoken Tutorials in Online Courses

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