Embedding an Intelligent Tutor into existing Business Software to provide On-the-Job Training
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Traditional on-the-job training for software typically involves human tu- toring, training videos, manuals and open-ended exploration of the software. This Masters Thesis investigates embedding a constraint-based intelligent tutor into an existing business software system to enhance the training op- tions for new users. Chreos Tutor, developed using the ASPIRE authoring system, is embedded into Chreos business software. It provides opportunities for users to practice two di erent types of data input tasks in the actual soft- ware environment. The student interface in Chreos Tutor is the combination of a new tutoring screen and existing data input screens. The main learning tool of a constraint-based tutor is the feedback on performance errors. In order to evaluate the e ectiveness of the feedback provided by Chreos Tutor, the experimental group were given feedback pertaining to individual errors in the submitted solution, whereas the control group received no feedback while working on a task. Analysis of pre-test and post-test results indicated that participants who received feedback while working on a task achieved a higher learning gain than participants who were presented with the ideal solution after submitting their solution. This suggests that Chreos Tutor is e ective at teaching data input tasks to new users.