Collect scores for your submissions by asking your reviewers any number of scoring questions.
Give numeric scores friendly labels like Excellent, Good etc to help reviewers with their assessment.
Score the quality of different criteria e.g. quality of content, relevance, quality of English etc. and weight the scorings so that the strongest criteria carry most significance when it comes to making decisions.
Combine scores from multiple reviewers using Averages, Standard Deviation etc. to give you a complete picture of your submissions.
Customise the numerical score labels.
Collect scores on multiple criteria.
Give different criteria different weighting.
Combine and tally scores for easy decision making.
For most scoring, a numerical scale will provide a clear means for grading a submission.
But, sometimes, numbers alone are not enough and textual descriptions can make the scoring much more tangible for reviewers.
Within Firebird, each numerical score can be given its own label to help reviewers make their choice. Scores can be displayed alongside the labels or hidden as required
In this example, the numerical value for the score is hidden to reviewers:
You can collect scoring and feedback on any number of criteria in your review forms.
Any number of criteria can be included and each criteria can be given its own weighting to give an accurate summary of the submission.
Examples of criteria that could be scored include:
Scores collected in the review form can be weighted, as required, to produce the most meaningful information for your submissions and reviews.
These scores can then be combined into a total score that is correctly weighted. This means that a single combined score can be quickly assessed to determine the quality of any one submission and to make decisions about that submission.
In this example, a review has two questions:
Each questions allows the reviewer to choose from a range of multiple choice options in their review form. In this example, the scores for each question follow a simple linear scale with no weighting:
However, it may be that Technical Merit is more important than Relevance of Topic when assessing a submission and so the individual scores could be given a weighting. Also, it may be that Excellent Technical Merit needs a stronger weighting so that it stands out from the rest even if the submission is not relevant.
So, this revised scoring could be implemented to give a better idea of how good a submission is for the project:
For each project, any number of questions, options and scores can be configured to help get the perfect scoring system.
Scores can be combined for a single review and also combined for a single submission.
This flexibility enables Firebird to collect and summarise the right data for fast and effective decision making.
In this example, a submission has 3 reviews and each review collects scores for two criteria (Technical Merit and Relevance of Topic) plus a recommendation from the reviewer:
|Relevance of Topic||2|
|Relevance of Topic||3|
|Relevance of Topic||3|
The reviews could have a total score and mean score calculated for them and the submission could have a mean score and Recommendation Tally:
|Combined Score||Review 1||Review 2||Review 3|
|Technical Merit Mean||7.33|
|Relevance of Topic Mean||2.67|
|Recommendation Tally||Accept x 2, Reject x 1|
The summary information in the submission can then be used for fast and effective decision making.
Every event is different and it can be confusing knowing what you need for your particular project.
We also understand that for first timers, the entire process can be extremely daunting which is why we provide more than software. For us, it's about giving you a personalised service too.
Come and talk to our experts who have personally supported thousands of events across every imaginable industry and academic subject.
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