Basics of qualitative research: Grounded theory procedures and techniques

Observing Interaction: An Introduction to Sequential Analysis, Roger Bakeman, John M. Gottman. Cambridge University Press, Cambridge (1986), ixv, +221. Price £25.00 hardback, £7.95 paperback

Applying data mining DM in education is an emerging interdisciplinary research field also known as educational data mining EDM. It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. © 2012 Wiley Periodicals, Inc.

Data mining in education

Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Data Center Energy Consumption Modeling: A Survey

Massive Open Online Courses (MOOCs) have experienced rapid expansion and gained significant popularity among students and educators. Although the broad acceptance of MOOCs, there is still a long way to go in terms of satisfaction of students’ needs, as witnessed in the extremely high drop-out rates. Working toward improving MOOCs, we employ the Grounded Theory Method (GTM) in a quantitative study and explore this new phenomenon. In particular, we present a novel analysis using a real-world data set with user-generated online reviews, where we both identify the student, course, platform, and university characteristics that affect student retention and estimate their relative effect. In the conducted analysis, we integrate econometric, text mining, opinion mining, and machine learning techniques, building both explanatory and predictive models, toward a more complete analysis. This study also provides actionable insights for MOOCs and education, in general, and contributes to the related literature discovering new findings.

https://pages.stern.nyu.edu/~padamopo/What%20makes%20a%20great%20MOOC.pdf

What Makes a Great MOOC? An Interdisciplinary Analysis of Student Retention in Online Courses

Encyclopedia of the Sciences of Learning

Direct support persons (DSPs) often face problems in observing and determining alertness in individuals with profound intellectual and multiple disabilities (PIMD). A literature study was carried out to gather information about the problems just described. A search of two electronic databases and the references found in relevant hits revealed 42 relevant publications. The results show that two types of descriptions of alertness can be distinguished: (1) those with a focus on the individual only or (2) those with a focus on the interaction of individual and environment. Several observation categories were used in the studies that were found. The reliability of the observations turned out to be a core problem in most of the studies. Only a limited number of environmental conditions that were expected to have an impact on alertness in individuals with PIMD were investigated. While modifications of the environment, interaction strategies, stimulation strategies and staff training were found to have a positive impact on alertness, studies about treatment activities led to conflicting results. Finally, we will formulate the resulting implications for future studies and for the development of an instrument for DSPs in order to observe alertness in individuals with PIMD in clinical practice.

Alertness in individuals with profound intellectual and multiple disabilities

Alertness in individuals with profound intellectual and multiple disabilities: a literature review.

The Tell-Tale: What Do Heart Rate; Skin Temperature and Skin Conductance Reveal about Emotions of People with Severe and Profound Intellectual Disabilities?.

In the support of individuals with profound intellectual and multiple disabilities (PIMD), assessing the level of alertness is a recurring issue for parents and other direct support persons. Although observations show clear advantages above and beyond other assessment methods, there are problems related to this method as well. Subjectivity of interpretation and low reliability results have been described as the main problems. In the present study, our aim was to estimate the reliability of the Alertness Observation List (AOL) while, at the same time, minimizing the problems entailed in observations. We calculated both the inter-observer agreement and intra-observer agreement for 39 situations. Since the results exceeded the formulated 80%-criterion, we concluded that the AOL was a reliable instrument. However, the large range found in the results was striking. Moreover, observers with different information about the observed individuals came up with different reliability scores. To determine the value of observation of individuals with PIMD, it might well be necessary to judge the actual usefulness that the instrument has in clinical practice, besides the reliability of the results.

Vera Munde

Papers

Encyclopedia of the Sciences of Learning

Alertness in individuals with profound intellectual and multiple disabilities

Alertness in individuals with profound intellectual and multiple disabilities: a literature review.

The Tell-Tale: What Do Heart Rate; Skin Temperature and Skin Conductance Reveal about Emotions of People with Severe and Profound Intellectual Disabilities?.

Determining Alertness in Individuals with Profound Intellectual and Multiple Disabilities: The Reliability of an Observation List