DSS for academic workload management

Transcription

1 Int. J. Management in Education, Vol. 3, No. 2, DSS for academic workload management Dejan Zilli* Nova Vizija, Information Engineering and Consulting, Vrečerjeva 8, 3310 Žalec, Slovenia *Corresponding author Nada Trunk-Širca Faculty of Management Koper, University of Primorska, Cankarjeva 5, 6104 Koper, Slovenia Abstract: Data from academic information system and other sources can be transformed and integrated into a data warehouse for the use of measuring, evaluating and planning academic workload. Our implementation case study involves a faculty with yearly workload of over 100,000 working hours. Results show that the process of academic workload management can be considerably improved with the use of an equity-weighing-based decision support system. Keywords: academic workload management; DSS; decision support system; management in education; data warehouse; WPS; workload planning system; AIS; academic information system. Reference to this paper should be made as follows: Zilli, D. and Trunk-Širca, N. (2009) DSS for academic workload management, Int. J. Management in Education, Vol. 3, No. 2, pp Biographical notes: Dejan Zilli specialises in data warehousing. He has over ten years of experience with projects in healthcare, industry, government and education institutions. He is in his final year of PhD study of computer science. Nada Trunk-Širca has a PhD in Management in Education. Since last 12 years she is in management position in higher education institution. Her research fields include management, quality and evaluations in higher education, and the life-long learning concepts. 1 Introduction Managing workload of faculty staff has always been a difficult task. Their work is independent and therefore difficult to plan and measure. To accomplish that task managers need timely, reliable and complete information about allocation of workload to faculty. Managers are responsible to allocate work to faculty in the most efficient way and to provide faculty with adequate compensation. The information about academic workload is usually distributed among different departments and information systems. Copyright 2009 Inderscience Enterprises Ltd.

2 180 D. Zilli and N. Trunk-Širca Even if this information is shared, it is still quite a challenge to use it in a way that can be helpful to university management. Getting around technical obstacles is usually the easier part. The difficult part is adopting the rules from university regulations and policies, which provide the complete overview of academic workload for the managers. A case study of building and implementing a Decision Support System (DSS) for the faculty with around 3000 students, 100 faculty staff members and over 100,000 working hours of yearly workload is presented in this paper. 2 Academic workload management: quick overview Academic workload management is a discipline which specialises in allocation of work to faculty members and in providing compensation for work done by faculty members. Hence several authors use the term faculty workload instead. To avoid confusion the term academic workload is used in this paper consistently. Doost (1997) discussed the raise of public interest for better accountability for university professors. Soliman (1999) presented a set of guiding principles for the allocation of academic workload and two models for measuring workload, one based on time and the other based on earnings. Academic staff responses to proposed principles and models were also considered. Comm and Mathaisel (2003) illustrated how information regarding academic workload, salary and benefits can be used to improve academic quality. Houston et al. (2006) presented the challenge that university faculty faces by increased accountability, and discussed several workload allocation models implementation issues. Keys and Devine (2006) surveyed faculty perception of effort associated with different teaching assignments. Managers goal is to achieve maximum productivity and quality of faculty work. The most difficult task is measuring different components of academic workload (Barlas and Diker, 2000). The principles of equity and transparency have to be considered to achieve optimal distribution of workload between faculty staff (Burgess et al., 2003). Faculty must do more than just teach and do research work in order to successfully fulfil their job obligations (Gappa et al., 2007). The proportion between teaching and research, as two main components of faculty work, varies according to the tenure status of faculty and the type of institution. Other professional and administrative activities are also necessary to accomplish required tenure status and to satisfy external pressures for accountability. Measuring academic workload to provide equitable workload distribution and adequate compensation, consequently improving academic quality, is the most important task of academic workload management. This implies the use of some kind of performance rating system (Burkholder et al., 2007) based on university regulations and policies. 1,2,3 Some higher education institutions apply faculty performance assessments plans. 4 Legal issues and faculty response to performance metrics must also be considered, especially when performance measures are used directly for salary calculation. Some authors used questionnaires to determine academic workload (Comm and Mathaisel, 2003). Cowdery and Agho (2007) used a mailed survey to asses methodologies used by different universities to determine and assign academic workload within health education. According to their study, most of the universities use credit hours as the main measure of academic workload (Stringer, 2007), while some use contact hours.

3 DSS for academic workload management Academic decision support system According to Turban et al. (2005), a DSS is a computer-based approach or methodology for supporting decision making. The most important part of a typical DSS is a data warehouse, which is subject-oriented, integrated, time-variant, non-normalised, non-volatile collection of data which enables analysing large amounts of data from various sources with rapid results. Decision support systems play an increasingly important role in higher education institutions. Doost (1997) described a potential academic workload database system. Deniz and Ersan (2001, 2002) proposed a DSS for student, course and programme assessment. Dasgupta and Khazanchi (2005) described intelligent agent enabled DSS for academic course scheduling. Vinnik and Scholl (2005) proposed an academic workload management DSS which focuses on academic capacity utilisation using a process of balancing educational demand and supply in universities. Important part of academic DSS is academic Workload Planning System (WPS) which focuses on balancing load against capacity. According to Burgess et al. (2003), strategic goals of WPS are as follows: teaching goals: 1 to maximise productivity (to minimise staff effort required to service a given level of funding) 2 to maximise quality (to maximise student choice of courses or modules) research goals: 1 to maximise research funding with given resources 2 to maximise grading of institution or unit Keys and Devine (2006) propose future development of practical system for academic workload management using equity weighting for workload assignments. In this paper, an implementation case study of such a DSS, based on academic workload data warehouse, is discussed. 4 Methodology The first step is requirements analysis which includes the analysis of faculty management perception of academic workload and analysis of workload regulations and policies. The next step, assessment of available data sources, is the most critical one in any data warehouse project. The fact that data sources of necessary quality are not available can bring a project to a stop. After that a data warehouse design using dimensional modelling approach is performed (Kimball, 1996). The next step is data transformation and cleansing, which is the most time-consuming part of any data warehouse project. After the data warehouse is designed and populated, multi-dimensional database can be built using On-Line Analytical Processing (OLAP) technology. After the prototype has been built it has to be tested. Involvement of faculty management and other faculty staff is necessary. Any technical or content issues have to be analysed. After the cause of the problem is determined the changes have to be made either on the prototype or on workload regulations and policies. The process of evaluation is never finished because circumstances which influence the extent of academic workload often change.

4 182 D. Zilli and N. Trunk-Širca 5 Requirements University management faces a difficult task of distributing workload among faculty taking into account many different aspects: faculty staff research profiles and area of expertise student numbers and their choice of subjects or modules research opportunities and grants transparent use of university workload policies faculty perception of equity of workload distribution public need for university professors accountability legal academic workload requirements. In Figure 1, the academic workload management process is presented. The first step is the assessment of workload. The second step is allocation of workload to faculty staff. Teaching workload allocation has to be compared with legal workload requirements (if any) in dependence of faculty staff habilitation (academic rank). Faculty commitment for accomplishing given tasks is the basis for compensation. During the term allocated workload has to be compared with actual workload to assist managers with workload reallocation when necessary. The difference between planed and actual academic workload has to be determined periodically, resulting in changes of employment contracts which ensure adequate compensation for faculty staff. Figure 1 Academic workload management process (see online version for colours) University workload regulations and policies have to be analysed thoroughly. Inconsistencies have to be eliminated in order to make the process of workload measurement completely uniform and transparent. If necessary, changes to workload regulations have to be proposed. Regulations have to be supplemented with additional documentation of used workload metrics. Different academic workload components have to be included. The use of equity weights in work hours makes measured workload components additive. When accurate data is not available, some presumptions have to be considered. Table 1 shows the list of proposed academic workload components.

5 DSS for academic workload management 183 Table 1 Academic workload components

6 184 D. Zilli and N. Trunk-Širca 6 Building a data warehouse The most important prerequisite for building a data warehouse is presence of quality data source. Adelman et al. (2005) described several problems which have to be assessed when dealing with dirty data: completeness (assessment yields identification of missing data sources) consistency (assessment results in definition of exception handling routines) correctness (assessment results in definition of error detection and correction routines). The main data source for academic workload data warehouse is Academic Information System (AIS) (Sastry, 2007). In general, AIS supports all the necessary functions university has to perform in education process. Some of the processes like workload planning are usually not sufficiently supported. When missing data is found it has to be determined which data source is the most appropriate for supplementation of main data source. Missing data can be derived from another university information system or from outside data sources, and then integrated with data from AIS. If no such possibility exists there is a need for AIS upgrade. If that is also not possible the use of different AIS has to be considered. A direct data entry into the data warehouse can be a temporary solution, but it is only feasible when changes of missing data happen periodically, for example once a year. When exceptions to general rules in the process of data transformation are found, they have to be evaluated to prevent the loss of transparency. When necessary, exception handling routines have to be put in place and documented. All exceptions have to be handled automatically. The same holds for the whole data transformation procedure, which has to be fully automated. A data warehouse has to be designed for easy detection and correction of errors in used data sources. Any information derived from DSS based upon the data warehouse must be traceable to a single data source item. After the correction of incorrect data, information in DSS has to change accordingly. Figure 2 shows a simplified dimensional model of academic workload data warehouse. The fact table contains semi-additive measure Quantity in workload unit and additive measure Workload hour. The Data version dimension contains one or more members for planned workload and one member for actual workload. Several of the presented dimensions are slowly changing dimensions, particularly Faculty staff dimension.

7 DSS for academic workload management 185 Figure 2 Academic workload data warehouse design 7 Implementation of DSS Standard OLAP browsing tools were used for the DSS interface. Before the adoption of DSS, some metrics and Key Performance Indicators (KPIs) were used to measure academic workload. Those metrics and KPIs were included as calculated measures, so that users of the DSS could start working with familiar content. After the circumstances for determining the extent of academic workload change, workload regulations and policies have to be changed accordingly. At the same time analysis of DSS change feasibility has to be performed resulting in appropriate changes in data transformation routines and changes in AIS if necessary. Faculty acceptance of automation introduced into academic workload measurement process is one of the key success factors for the project. To achieve faculty acceptance WPS has to be transparent and has to assure equity of workload allocation (Burgess et al., 2003).

8 186 D. Zilli and N. Trunk-Širca Trial work is basis for successful implementation of any new information system (Natek and Lesjak, 2006). In this project, faculty were first given reports of their planned workload, according to workload policy. Any unclear items of their workload reports were then discussed and explained. Any disparities with workload policy were eliminated before the use of DSS had any consequence on faculty compensation. During academic year the same workload items were then measured and compensated for. So what did we achieve with DSS implementation? Faculty are now included in workload planning process and can therefore understand the strategic goals of the university much better. That is the first step towards alignment of staff behaviour with the strategic goals of the higher education institution. 8 Conclusion An academic workload management data warehouse has been built to provide university managers with appropriate decision support. University management is equipped with tools which enable them to plan and allocate academic workload better and to provide adequate compensation for their faculty staff. The necessary steps for building such a data warehouse are discussed. The importance of the adoption of university academic workload policy is stressed. The implications of use of the designed DSS on faculty staff are considered. Discussed DSS implementation focuses on the workload policy of a single faculty. In future, we plan to include all the other faculties of the university. In spite of open DSS design, some changes are expected dependent upon different academic workload policies. The differences in academic workload policies will be analysed and consolidated. Thereby the university management will be provided with one single picture of academic workload for the whole university, as well as with all necessary information for exchange of faculty staff between faculties and departments. As a result the university will be able to make a better use of their human resources. The other future challenge is changing the main data source. The results of this project made it clear that the existing AIS needs quite a few changes to provide all the needed information for the workload planning process. Since the necessary support was unavailable, the change of AIS was proposed. This change should not affect the logical data warehouse design; however, it will trigger the need for the change of the majority of the data transformation procedures. The change of AIS should be completely transparent for the users of the DSS. Any academic workload information from the DSS should be presented independently of its underlying data source. References Adelman, S., Moss, L.T. and Abai, M. (2005) Data Strategy, Pearson Education, Inc., Upper Saddle River, New Jersey. Barlas, Y. and Diker, V.G. (2000) A dynamic simulation game for strategic university management (UNIGAME), Simulation Gaming, Vol. 31, pp Burgess, T.F., Lewis, H.A. and Mobbs, T. (2003) Academic workload planning revisited, Higher Education, Vol. 46, pp Burkholder, N.C., Golas, S. and Shapiro, J. (2007) Ultimate Performance: Measuring Human Resources at Work, John Wiley & Sons, Inc., New Jersey.

9 DSS for academic workload management 187 Comm, C.L. and Mathaisel, D.F.X. (2003) A case study of the implications of faculty workload and compensation for improving academic quality, International Journal Educational Management, Vol. 17, No. 5, pp Cowdery, J.E. and Agho, A. (2007) Measuring workload among health education faculty, Californian Journal of Health Promotion, Vol. 5, No. 3, pp Dasgupta, P. and Khazanchi, D. (2005) Adaptive decision support for academic course scheduling using intelligent software agents, International Journal of Technology in Teaching and Learning, Vol. 1, No. 2, pp Deniz, D.Z. and Ersan, I. (2001, August) Using an academic DSS for student, course and program assessment, International Conference on Engineering Education, Oslo, pp.6b8-12 6B8-17. Deniz, D.Z. and Ersan, I. (2002), An academic decision-support system based on academic performance evaluation for student and program assessment, International Journal of Engineering Education, Vol. 18, No. 2, pp Doost, R.K. (1997) Faculty evaluation: an unresolved dilemma, Managerial Auditing Journal, Vol. 12, No. 2, pp Gappa, M.G., Austin, A.E. and Trice, A.G. (2007) Rethinking Faculty Work: Higher Education s Strategic Imperative, John Wiley & Sons, Inc., Jossey Bass, San Francisco. Houston, D., Meyer, L.H. and Paewai, S. (2006, March) Academic staff workloads and job satisfaction: expectations and values in academe, Journal of Higher Education Policy and Management, Vol. 28, No. 1, pp Keys, A.C. and Devine, M.M. (2006) Faculty perceptions of teaching load, Issues in Information Systems, Vol. VII, No. 1, pp Kimball, R. (1996) The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses, John Wiley & Sons, Inc., New Jersey. Natek, S. and Lesjak, D. (2006) Trial work: the way to successful information system projects in healhcare, International Journal of Electronic Healthcare, Vol. 2, No. 3, pp Sastry, M.K.S. (2007) Development of an academic information system fro effective education management, International Journal of Management in Education, Vol. 1, No. 3, pp Soliman, I. (1999, July) The academic workload problematic, HERDSA Annual International Conference, Melbourne, Australia. Stringer, M. (2007) Department head leadership and the use of credit hours as a measure of faculty workload, A Dissertation Presented to The Faculty of the Graduate School, University of Missouri, Columbia. Turban, E., Aronson, J.E. and Liang, T.P. (2005) Decision Support Systems and Intelligent Systems, 7th ed., Pearson Education, Inc., Upper Saddle River, New Jersey. Vinnik, S. and Scholl, M.H. (2005) Decision support system for managing educational capacity utilization in universities, ICECE05, International Conference on Engineering in Computer Education. Notes /workload_policy.pdf 4