A Data Warehouse Model for Micro-Level Decision Making in Higher Education

Liezl van Dyk

University of Stellenbosch, South Africa
lvd@sun.ac.za

Abstract: An abundance of research, by educational researchers and scholars of teaching and learning alike, can be found on the use of ICT to plan design and deliver learning activities and assessment activities. The first steps of the instructional design process are covered quite thoroughly by this. However, the use of ICT and quantitative methods to close the instructional design cycle by supporting sustainable decision making with respect to the evaluation of the effectiveness of teaching processes hold much unleashed potential. In this paper a business intelligence approach is followed in an attempt to take advantage ICT to enable the evaluation of the effectiveness of the process of facilitating learning. The focus is on micro-level decision support based on data drawn from the Learning Management System (LMS). Three quantifiable measures of online behaviour and three quantifiable measures of teaching effectiveness are identified from literature to arrive at a 3x3 matrix according to which 9 measures of e-teaching effectiveness can be derived by means of pair-wise correlation. The value and significance of information are increased within context of other information. In this paper it is shown how the value of LMS tracking data increases within context of data from other modules or others years and that useful information is created when this tracking data is correlated with measures of teaching effectives such as results, learning styles and student satisfaction. This information context can only be created when a deliberate business intelligence approach if followed. In this paper a data warehouse model is proposed to accomplish exactly this.

Keywords: learning management system, data warehouse, student tracking, decision support, student feedback,
learning styles

1.Introduction
In a paper, commissioned by the EDUCAUSE Centre for Applied Research, Goldstein & Katz (2005) coined the terminology Academic Analytics to refer to Business Intelligence within an Educational setting. They argue that Business Intelligence “rang hollow to our delicately trained academic ears”. Business Intelligence entails the gathering of data from internal and external data sources, as well as the storing and analysis thereof to make it measurable, so as to assist and sustain more efficient and longitudinal decision-making (Kimball, 2002 and Imnon et al., 2001).

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Attaining Social Value from Electronic Government

Michael Grimsley1, and Anthony Meehan2
1Faculty of Arts, Computing, Engineering and Sciences, Sheffield Hallam University, UK
2Centre for Research in Computing, Department of Computing, The Open University, UK
m.f.grimsley@shu.ac.uk
a.s.meehan@open.ac.uk

Abstract: We define and elaborate a Social Value framework supporting evaluation and attainment of the broader sociopolitical and socio-economic goals that characterise many electronic government initiatives. The key elements of the framework are the willingness of citizens to (positively) recommend an e-Government service to others, based upon personal trust in the service provider, and personal experience of the service, based upon experience of service provision and outcomes. The validity of the framework is explored through an empirical quantitative study of citizens’ experiences of a newly introduced e-Government system to allocate public social housing. The results of this study include evidence of generic antecedents of trust and willingness to recommend, pointing the way to more general applicability of the framework for designers and managers of electronic government systems.

Keywords: electronic government, social value, public value, recommendation, trust, evaluation.

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Network Marketing's Greatest Gift

Hint: It's Not the Products or the Income

By Robert T. Kiyosaki

After retiring in 1994, financially free at the age of 47, I began to research the network marketing industry. Whenever someone invited me to a presentation, I would go, just to hear what they had to say. I even joined a few—but not necessarily to make more money. I joined in order to take a long, hard look at the positives and negatives of each business.

After working my way through masses of wannabes, hustlers and dreamers, I began to meet the leaders of some of these businesses. The ones I met were some of the most intelligent, kind, ethical, moral and professional people I have met in all my years of business. Once I got over my own prejudices and met people I could respect and relate to, I found the heart of the business.

I am often asked, “If you did not become rich and famous from a network marketing business, why do you recommend people get into the business?”
It is because I did not gain my fortune from network marketing that I can be perhaps a bit more objective about industry and its real value—a value that goes beyond the potential of making a lot of money.
It’s Not the Money
“We have the best compensation plan

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The Emergence and Diffusion of the Concept of Knowledge Work

Hanna Timonen and Kaija-Stiina Paloheimo
Helsinki University of Technology, Finland
hanna.timonen@tkk.fi
kaija-stiina.paloheimo@tkk.fi

Abstract:

The past decades have witnessed the proliferation of research on knowledge work. Knowledge work has mostly been used as an antonym to manual work, to refer to specific occupations characterized by an emphasis on specialized skills and the use of theoretical knowledge. The efforts to encompass all the different contexts where knowledge plays a relevant role in work tasks has resulted in various and ambiguous definitions of what knowledge work actually is.

In order to shed light on the elusive concept of knowledge work, we studied how it has appeared in the scientific discussion, and diffused from one scientific community to another. As the circulation of new ideas and concepts in scientific discussion is apparent through academic literature, we examined the emergence and diffusion of the concept of knowledge work through a citation analysis on articles from the Social Sciences Citation Index. The data set consists of 273 articles with 7,057 cited references for the 1974 to 2003 period, and we used a dense sub-network grouping algorithm on the co-citation network to distinguish highly cited groups of references.

We distinguish three periods of diffusion of the concept of knowledge work. The results show that Drucker’s In the age of discontinuity (1969) and Bell’s The coming of post-industrial society (1968) were the main influencers when the concept emerged in the scientific discussion from 1974 to 1992. After this period, we can distinguish a slow diffusion period from 1993 to 2003, when the concept started to gain attention, and a fast diffusion period from 1999 to 2003, when the research proliferated.

The discussion dispersed outside the management domain already in the emergence period, but the management domain has stayed the main domain of discussion also later on. However, from 1992 to 2003 the discussion inside the management domain dispersed into different groups. One of the main influences to a new group of research that appeared at this time was Zuboff’s In the age of the smart machine (1984). This group, drawing on research conducted on knowledge-intensive firms, has recently produced highly cited articles such as Blackler’s ‘Knowledge, knowledge work and organizations’ in Organization Studies (1995). As the current discussion on knowledge work is dispersed in different groups, there is a need to engage in a common conceptual discussion and define what is actually meant by knowledge work.

Keywords: scientific discourse, knowledge work, bibliometric analysis, citation analysis

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Nonlinear Image Recovery with Half-Quadratic Regularization

Donald Geman and Chengda Yang
July, 1993

Abstract

One popular method for the recovery of an ideal intensity image from corrupted or indirect measurements is regularization: minimize an objective function which enforces a roughness penalty in addition to coherence with the data. Linear estimates are relatively easy to compute but generally introduce systematic errors; for example, they are incapable of recovering discontinuities and other important image attributes. In contrast, nonlinear estimates are more accurate, but often far less accessible. This is particularly true when the objective function is non-convex and the distribution of each data component depends on many image components through a linear operator with broad support. Our approach is based on an auxiliary array and an extended objective function in which the original variables appear quadratically and the auxiliary variables are decoupled. Minimizing over the auxiliary array alone yields the original function, so the original image estimate can be obtained by joint minimization. This can be done efficiently by Monte Carlo methods, for example by FFT-based annealing using a Markov Chain which alternates between (global) transitions from one array to the other. Experiments are reported in optical astronomy, with Space Telescope data, and computed tomography.

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Short Paper: Mobile Proactive Secret Sharing

David Schultz
Massachusetts Institute of Technology
das@csail.mit.edu

Barbara Liskov
Massachusetts Institute of Technology
liskov@csail.mit.edu

Moses Liskov
The College of William and Mary
mliskov@cs.wm.edu

ABSTRACT

MPSS is a new way to do proactive secret sharing in asynchronous networks. MPSS provides mobility: The group of nodes holding the shares of the secret can change at each resharing, which is essential in a long-lived system. MPSS additionally allows the number of tolerated faulty shareholders to change when the secret is moved so that the system can tolerate more (or fewer) corruptions; this allows reconfiguration on the fly to accommodate changes in the environment.

Categories and Subject Descriptors

C.2.4 [Computer Communication Networks]: Distributed Systems--distributed applications

General Terms

Security

1. INTRODUCTION

Malicious attacks are an increasing problem in distributed systems. If a node holds an important secret, that secret could be exposed by an attack in which an intruder gains control of that machine. An example of such a secret is the private key used by a certificate authority (such as Verisign) to sign its certificates.
Secret sharing allows a group of servers to possess shares of a secret, such that any t + 1 servers can collaborate to compute with the secret, but any t or fewer servers can learn nothing about the secret. Proactive secret sharing extends secret sharing to work in a long-lived system, in which nodes can become compromised over time, allowing the adversary to collect more than t shares and recover the secret. These schemes provide a share regeneration protocol, in which a new set of shares of the same secret is generated and the old shares discarded, rendering useless any collection of t or fewer old shares the adversary may have learned.

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Friendship Versus Business in Marketing Relationships

Although combining friendship and business in the same relationship can be beneficial, it can also create conflict.
A source of this conflict is incompatible relational expectations. True friends are expected to be unmotivated by
benefits that can be used beyond the relationship (e.g., money, status), whereas business partners are, by
definition, at least partly motivated by these more “instrumental” concerns. Using a role theory framework and data
collected from a survey of 685 direct-selling agents, this article reports evidence that a conflict between friendship
and instrumentality can undermine some of the business outcomes that friendship might otherwise foster. It also
suggests that this conflict is more severe for friendships that become business relationships than for business
relationships that become friendships. Study conclusions do not suggest that friendship is entirely “bad” for
business and, instead, propose that friendship’s influence can be both positive and negative.

Social Roles and Role Conflict
This article uses a role theory perspective to analyze and
understand the conflicting orientations of friendships and
business relationships. Several previous studies in marketing
have also applied this framework (Arnett, German, and
Hunt 2003; Atuahene-Gima and Li 2002; Schewe 1973;
Singh 2000; Smith and Barclay 1997; Solomon et al. 1985;
Walker, Churchill, and Ford 1977), including Heide and
Wathne’s (2006) recent analysis of friendships and business
relationships. Role theory is based on the premise that
effective social interaction depends on a shared understanding
of relationship rules—that is, the behaviors that are
(in)appropriate for different people in different social situations
(Biddle 1986; Heide and Wathne 2006; Michaels, Day,
and Joachimsthaler 1987; Sarbin and Allen 1968).

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