Generate charts/graphs for a dashboard from one of the data set provided, with a focus on what a teacher might find useful.
- Data: Sample data 2.
- Software: Excel.
A 12-week postgraduate class, with the possibility for on-campus and online attendance. Resources, such as the Course Handbook, are shared through the VLE (including a link to Resource Lists, accessible through the Library).
The course is summatively assessed by four separate tests, each with a pass mark of 40%, of which one Fail (below 40%) may be condoned. The overall course mark is an average of the four individual marks, and also has a pass mark of 40%. It is not credit-bearing, but those who pass are offered a certificate of completion. Attendance at on-campus activities is not compulsory.
Firstly, there is no contextual data in the dataset; we have no personal details nor anything about their previous academic history. Some overall summary statistics might be of interest to the teacher.
Such charts could assist in seeing if the level of the assessment appeared to be appropriate for the cohort and if it provided differentiation. Whether the assessment actually was successful in determining if the students reached the aims of the course, would require closer examination of the assessments themselves.
The teacher might be happy that all students passed. However, this does not give any indication of why students performed as they did, or if the assessment is not just an indication of factors that are not about academic ability.
Any teacher would be interested in outliers, especially those who indicate that they are not thriving for any number of reasons; of course, interest would only be of real good if realised early in the course (or better still, as part of the course/curriculum design). Student S0021 can be seen to have the lowest overall test scores, attendance (on campus and online), and online engagements, according to the data provided; S0004 the highest.
There does appear to be a correlation between in-class attendance and test score but that does not mean there is an causation: the reasons that a student does not attend could be the same or related to the reasons that they find it hard to study. Of course, this all assumes that the student with the lowest score did badly; given their previous academic ability, say, this may constitute a considerable achievement. The student with the highest score may be automatically considered to have done well, but their mark may not be a true reflection of their ability.
Class attendance, on-campus or online, is easily measured but the reasons behind it and its actual effect on the student are not easily known. Graphs like this however can be easily misread.
The teacher may be interested to know if students who attend on-campus tend not to attend online, and vice versa. The data does not appear to suggest this. However, this is based on number of log-ins to the VLE which is a poor proxy for online engagement, though counting attendance at on-campus sessions may not be much better.
Again, our student S0002, may have taken this course because they do not have to attend on-campus, and in that one visit to the VLE may have downloaded their Handbook and complete reading list, and subscribed to the discussion boards (so updates come to them via email), meaning that they do not have to log in again.
The one occasion where lack of online activity may be worth investigating is at the start of a course, where lack of activity may mean the student is experiencing initial technical difficulties or has not received log-in details, and due to this, may also find it difficult to access help.
The teacher may be interested in finding if involvement in the discussion online is in any way coorelated to test scores; not surprising if the teacher has put a considerable amount of effort into this themselves. The data suggests not, but it is only about whether students read, posted or replied, not the quality of the experience they had. Again, graphs such as this can be very suggestive if they draw attention to the activity of the highest and lowest scoring students, and if this is used to explain past outcomes or predict future ones.
Having access to a data set, invites curiosity, especially if one has a connection to it. Insufficient knowledge of the amount and kind of data needed, does not stop one from creating what can be a persuasive visual display of ‘knowledge’.