Calculating the Departmental Credit-Hour Cost for Higher Learning Institutions Using Joint Costing and Activity-Based Costing Systems Simultaneously
Bibliographic record
Abstract
The question of how to calculate the effective credit hour costs for different departments in Higher Learning Institutions was approached in this paper using Joint Costing and Activity-Based Costing techniques. The cost of the effective credit hour in the higher learning institutions was treated as joint cost problem. The main advantage of joint cost analysis is its ability to handle multiple faculties who are using common resources up to achieve split off so that each faculty has its own separable cost. The departments within the faculty were also treated as joint cost problem as these departments use common resources up to their split off point as well. The Activity-Based Costing system (ABC) then was used because of its ability to allocate the joint costs to the corresponding faculties and departments. Furthermore, the separable costs pertaining the different departments were added to calculate the departments’ costs. We suggest that the annual effective departmental credit-hour cost to be calculated by dividing the annual total cost of the department by the annual effective number of credit hours taught in that department. The Knapsack model was applied at each cost level to determine the optimal cost driver set for the Activity-Based costing analysis such that a tradeoff between the precision and the cost of the information obtained from the analysis was reached. The proposed model was explained using a hypothetical example of a university containing 9 faculties such that the costs incurred for the university were decomposed into four levels: Facility level and it included all the costs that were not directly related to any of the faculties or departments, Product level and it included all the costs that were related to a certain faculty and not related to a specific department within that faculty, Batch level and it included all the costs that were directly related to a specific department, and finally, the Unit level and it included the annual effective number of hours registered in a department. Originally 12 cost drivers were considered for this hypothetical problem, and then a binary programming model utilizing the Knapsack setup was used to select an optimal set of 9 cost drivers such that those who are not selected were combined with the ones that were selected. The results showed that the proposed method offered precise information about the annual departmental credit-hour cost for higher learning institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".