Assigning Students to Groups for Class Projects: An Exploratory Test of Two Methods

Beheshtian-Ardekani and Mahmood [1] recently offered a means of assigning students to groups for class projects to achieve a balanced distribution of student skills within groups. They assumed- but did not show-that the instructor would give weaker students a better chance to learn from stronger group members, that there would be greater synergistic learning effects, and that students would be happier with the project experience. This experiment tests the balanced-group method of assigning students using the people-sequential heuristic by comparing its effectiveness against the results achieved by random assignment to groups. The findings show that balanced groups have a modest advantage over groups formed randomly. Students in balanced groups felt slightly more satisfied with and challenged by the group and shared the work load more evenly. Within randomly assigned groups, student perceptions of the quality of the group-project learning experience were less homogeneous.     

A STATISTICAL ANALYSIS OF THE UTILIZATION EFFECTIVENESS OF A PERT PROGRAM

This article is based on the belief that students should learn the computational aspects of decision science techniques outside the classroom, thus freeing the instructor from this task and enabling him to devote more class time to focusing on the theoretical and/or application-oriented elements which will take students further in their learning experience. An experiment was designed to determine the learning value of one way of achieving this, namely the use of a Learning Center where students learned individually using a programmed instruction learning machine. The research involved both graduates and undergraduates learning the fundamentals of PERT—some with the learning machine approach while others were involved in more conventional pedagogies. Surprising success was discovered relative to both the approach and the quality of the PERT program used, with far-reaching implications regarding futuristic approaches to decision science education.     

An Analysis of Pedagogical Effects of Alternative Teaching Approaches of Goal Programming

Lee, Shim, and Lee [12] suggested the superiority of the signal flow graph (SFG) method over the modified simplex method in enhancing students' learning of goal programming (GP). The present study empirically investigated this claim by analyzing the effect of the SFG method of GP on students' learning. In the process, this study also developed an effective tool for measuring GP knowledge. The findings showed that students who learned the SFG method understood GP better and more easily than those who learned the concept through the modified simplex method. The students who learned the SFG method of GP were also less dependent on rules or formulas than the students who learned the modified simplex method.     

The Effects of Memory Structure on Using Rule-Based Expert Systems for Training: A Framework and an Empirical Test*

One justification for eliciting and representing the judgment strategy of an expert in a rule-based expert system (RBES) is to facilitate knowledge transfer to individuals with less expertise. However, prior research suggests complexities and potential problems when using RBESs for training. In this paper, a conceptual framework of user learning from RBESs is presented. It is proposed that learning may be ineffective when the problem representation of the RBES is inconsistent with the user's mental representation of the task-domain knowledge. An experiment was conducted to examine the effects of consistency (inconsistency) between the problem-solving strategy of RBESs and individuals' memory structures. Groups of subjects whose memory structure either matched or did not match two RBESs' problem-solving strategies were examined using an internal control evaluation task. The results suggest that learning was facilitated only for groups with congruence between the RBES's problem-solving strategy and a subject's memory structure.     

An Investigation into the People-Sequential Heuristic Method of Group Formation

This study investigates the people-sequential heuristic method of group formation as proposed by Beheshtian-Ardekani and Mahmood [2] and examined by Muller [9]. Responding to Muller's call for continued research on this topic, we first replicate the seven hypothesis investigations presented in that study and then consider several additional hypotheses designed to more fully examine the value of the process to the group members themselves. The results of this study indicate that the balanced groups assignment method is not effective as a sole means of increasing learning or satisfaction with the project. Of the twelve hypothesized relationships, which, if supported, would favor the use of balanced groups over randomly assigned groups, only one was supported. These findings and their relationship to group learning theory are discussed.     

Integrating Neural Networks and Semi-Markov Processes for Automated Knowledge Acquisition: An Application to Real-time Scheduling*

Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi-Markov optimization, and ANN techniques for automated knowledge acquisition in real-time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non-intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi-Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN-based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real-time scheduling. The results indicate that ANN-based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.     

Integrating Analysis and Communication Skills into the Decision Science Curriculum*

The teaching of decision sciences presupposes that careful attention is directed toward improving students' critical thinking and analytical skills. This paper presents an approach to teaching quantitative methods classes that focuses on developing student skills in the areas of analytical and critical thinking through written and oral communication. The approach requires very little additional instructor preparation time and does not require the instructor to make major changes in either the content or organization of the course. While the instructor's investment is minimal, the benefits to the students appear to be substantial.     

Building Strategic Technology Alliances for Teaching and Learning

There is a major need for higher education to confront intense competition, enhance academic programs to meet the challenges of this competition, and to develop new teaching and learning strategies to meet the demands of the global marketplace. Technology and the Internet are intended to break down the barriers of time and space and enable more students to participate and collaborate with faculty and other students. Critical thresholds must be met in order to use technology to communicate, collaborate, and transform teaching and learning. Strategic technology alliances establish a partnership between technology vendors and higher education that will achieve these thresholds to meet the teaching and learning needs of the 21st century student. This revised version was published online in July 2006 with corrections to the Cover Date.     

A UNIVERSITY EXAMINATION-SCHEDULING MODEL TO MINIMIZE MULTIPLE-EXAMINATION DAYS FOR STUDENTS

Scheduling university examinations is often done with the objective of spreading a student's required examinations over an examination week. That is the equivalent of the problem of minimizing the number of examinations a student must take on any one day. An approach to scheduling exams which relates the problem to the classical assignment problem is discussed. The model developed is a symmetry-constrained assignment model, and the solution method requires use of a branch-and-bound algorithm. Results from application of the algorithm to six semesters of actual data are presented.     

EFFECTS OF USE OF COMPUTER IN FIRST-YEAR STATISTICS SEQUENCE ON STUDENT ATTITUDES*

The use of computers in teaching statistics can make an impact upon students in two ways. First, it can affect the amount and rate of learning of statistics. Second, it can affect student attitudes toward computers, statistics and quantitative methods. This note reports a study on the effects of the computer in teaching statistics on student attitudes. The data are based on pre- and post-course questionnaires administered in a first-year statistics sequence. The results partly confirm expectations, and partly raise questions for further investigation.     

USING SHORT-TERM DEDICATION FOR SCHEDULING MULTIPLE PRODUCTS ON PARALLEL MACHINES

In a multiproduct, parallel machine environment, it may be beneficial to dedicate one or more of the machines to a single product for consecutive time periods. However, previous lot-sizing and scheduling models usually do not allow for such short-term dedication. This paper presents a mixed-integer programming model that allows for short-term dedication in scheduling parallel machines serving multiple products with dynamic demands. It also describes a Lagrangian-based algorithm for solving such scheduling problems. An experiment verifies the effectiveness of the algorithm and demonstrates the importance of allowing for short-term dedication. Capacity utilization, one of six factors investigated, greatly affects the use of dedication. A strong interaction effect between the number of machines and the number of products is also seen with respect to the usefulness of short-term dedication. A measure of estimated production frequency incorporates these two factors along with magnitudes of periodic demands. This measure has a strong relationship with the amount of short-term dedication used in solutions. Operations managers employing parallel machines could use the experimental results in deciding whether to use short-term dedication to reduce costs.     

Learning by Objectives for Adaptive Shop-Floor Scheduling*

Effective production scheduling requires consideration of the dynamics and unpredictability of the manufacturing environment. An automated learning scheme, utilizing genetic search, is proposed for adaptive control in typical decentralized factory-floor decision making. A high-level knowledge representation for modeling production environments is developed, with facilities for genetic learning within this scheme. A multiagent framework is used, with individual agents being responsible for the dispatch decision making at different workstations. Learning is with respect to stated objectives, and given the diversity of scheduling goals, the efficacy of the designed learning scheme is judged through its response under different objectives. The behavior of the genetic learning scheme is analyzed and simulation studies help compare how learning under different objectives impacts certain aggregate measures of system performance.     

MOTIVATING STUDENTS IN THE OPERATIONS MANAGEMENT CLASS: CHALLENGES FOR THE PUBLISHING INDUSTRY

Motivating students to learn and apply operations management concepts is an important aspect of the learning process in an operations management class. While an emphasis on techniques and pedagogical technology has some motivational benefits, this paper proposes an emphasis on student/teacher interaction and a deeper understanding of complex situations. To support this approach, publishers should provide the materials to effectively use the new classroom technologies and the tools to support a wide variety of teaching styles. In addition, publishers should provide creative cross-functional simulations so that students can understand the role of operations management in the context of the firm. Finally, rich decision-making environments are needed to put the students in more realistic situations.     

Earning While Learning: When and How Student Employment is Beneficial

Although studies of student employment (‘earning while learning’) mostly find positive wage effects, they do not adequately consider the relation of the employment to the field of study. We investigate how different types of student employment during tertiary education affect short‐ and long‐term labour market returns. Beyond examining differences between non‐working and part‐time working students, we distinguish between student employment related and unrelated to the field of study. Our results show significant positive labour market returns of ‘earning while learning’ only for student employment related to the field of study. These returns consist of a lower unemployment risk, shorter job‐search duration, higher wage effects, and greater job responsibility.     

Educating collaborative planners: strengthening evidence for the learning potential of multi-stakeholder regional learning environments

Abstract

Planning education needs effective learning environments that support students’ boundary crossing competence development. The multi-stakeholder regional learning environment (RLE) is, by its typical design, hypothesized to foster boundary crossing. This quasi-experimental mixed method pre- and post-test study affirms the effectiveness of the RLE for stimulating competence development, and specifically shows the added value of three typical ‘boundary crossing’ RLE design characteristics, i.e. working in multidisciplinary student groups, working intensively with multiple stakeholders, and a high coaching intensity. Results strengthen previous findings, and contribute to the debate in planning education on an evidence-based pedagogical fundament for commonly used authentic learning environments.     

THE USE OF INDUSTRIAL SOFTWARE TO CREATE EXPERIENTIAL LEARNING ACTIVITIES IN OPERATIONS MANAGEMENT COURSES

Computer-based manufacturing planning and control (MPC) systems are widely used in industry to gain competitive advantage through integration and coordination of managerial activities. In collegiate business schools, important operations management activities are taught and studied, often by sequential examination of discrete topics such as aggregate production planning, master production scheduling, capacity planning, material planning, and production activity control. This paper explores the potential use of industrial MPC software in the classroom to create experiential learning activities that address the dynamic and integrative nature of operations management. Experiences with this pedagogical approach over the past decade are reported.     

Shop floor characteristics influencing the use of advanced planning and scheduling systems

The purpose of this article is to investigate how the manufacturing process, the shop type and the data quality, i.e. the shop floor characteristics, influence the use of advanced planning and scheduling (APS) systems in production activity and control (PAC). The methodology implemented is a multiple case study at three case companies. Each company has different shop floor characteristics, but all use a scheduling module in an APS system, which supports production scheduling. A theoretical framework is developed suggesting how APS system are used in the PAC activities, and which major aspect to consider. The case analysis shows that the scheduling module in APS system, foremost supports sequencing and dispatching. In particular, the shop type is influenced by the decision of how often the APS runs and what freedom is given to the shop floor. The manufacturing process influences how the dispatch list is created. Contrary to the literature presuming that APS systems are most suitable in job shop processes, it is found that the manufacturing process is not a crucial factor when deciding whether APS systems are an appropriate investment. It is found that the level of data quality needed in the APS system depends to a large extent on how the dispatch list is used. For example, is the dispatch list used as a guideline, not a regulation, the need for accurate data in the module is reduced. This article extends the previous literature concerning APS systems by analysing how APS systems influence PAC as a whole and increase the understanding of the challenges of using APS systems in PAC.     

Interface Style and Training Task Difficulty as Determinants of Effective Computer-Assisted Knowledge Transfer*

The transfer of expert knowledge to novices is one means of improving decision quality. Research needs to identify (1) the knowledge to be transferred to novices, and (2) the best method for transferring that knowledge. Studies that compare the judgment behavior of experienced and novice auditors address the first issue. The present study addresses the second issue in the context of using a computer-assisted training (CAT) program. CAT was selected for study because of evidence that it can both improve the effectiveness and reduce the costs of training. An experiment was conducted in which two factors were manipulated: (1) the design of the human-computer interface of the CAT program, and (2) the content of training tasks. The judgment of interest involved causal reasoning about the relationships between various internal control procedures and possible errors. The results indicate that alternative styles of interaction with a CAT program differ in terms of learning effectiveness. In addition, there was also evidence that training task content affected learning.     

Increasing the Effectiveness of Student Evaluation of Instructor Data through a Factor Score Comparative Report

Effective student evaluation of instructor instruments must provide norm, or comparative, data to aid interpretation. This note highlights an innovative factor score comparative report. Rather than merely reporting comparative data on individual questions items, the innovative report provides comparative percentile data on the six factors underlying our thirty-three item questionnaire. The faculty indicated overwhelming support for the new report. The note also briefly mentions three other activities that are essential for student evaluation of instructor data to improve teacher effectiveness. Our goal is to stimulate other colleges to adopt the innovative norming practice once they have validated their local evaluation instruments. College-wide improvement of teaching will occur through such do-it-yourself efforts and only decision science faculty are technically qualified to lead such efforts.     

Distance Learning: Education or Economics?

Sir John Daniel, Vice Chancellor of the Open University in England, pronounced that American universities are in a crisis of access, flexibility and cost. His solution is that American Universities and Colleges should employ educational technologies that allow students to earn college degrees without visiting the college campus (distance learning). The argument is that distance learning is a cost-effective way of providing more students with access to higher education. Sir John's argument illustrates a crisis in neither access, flexibility, nor cost. On further inspection, distance learning degree programs appear to be a strategy for reducing the cost of higher education by replacing professors with computers and part-time teachers. This strategy offers limited educational benefits for the student and may present a financial danger to smaller colleges and universities, if they succumb to the pressure to develop distance, learning degree programs. This revised version was published online in July 2006 with corrections to the Cover Date.