Please select a Scenario from the four below:
Scenario 1: Personalised emails with if-this-then-that rules
Mike is the coordinator of a unit of study in which enrolment has been steadily increasing over the past years. Alice is an educational designer and is approached by Mike to see if she can set up OnTask to provide students in his class with some suggestions through email as the semester advances. Alice opens the URL of OnTask and starts a session with her university credentials. After creating a matrix for the course, she uploads a CSV file with the names, emails and ids of the students currently enrolled in the course. Alice shares the matrix with Mike giving him access within OnTask.
Mike is using a couple of tools that track how students are interacting with the course resources. These tools provide CSV files with one student per row, and a set of indicators of activity as columns. Mike uses OnTask to upload this CSV files and then write a set of “if-this-then-that” rules enunciated in terms of the indicators captured by these tools and to decide to include or not certain text snippets in a personalised email that will eventually be sent to the student. For example, if a student has not read the course syllabus, then the following text is included in the email message:
“Make sure you read the course syllabus as it contains important information related to the learning objectives, assessment, and administrative aspects.”
Alice helps Mike create rules that will include suggestions for the students to make sure they use efficiently the resources in the course, are aware of deadlines, and ask for help if needed. Alice has read some examples included with OnTask and after taking a few notes from Mike about the course structure, she creates a set of initial rules to detect basic student situations (syllabus not read, didn’t post in the forum, etc.) and then shares them with Mike. Some rules will depend on a combination of results obtained from the grade book in the LMS and the indicators obtained from other platforms. The personalised emails will include various sentences depending on the indicators available for each student.
Once a week Mike connects to OnTask, loads the CSV files with the indicators of student activities on the existing matrix, tests the rules to make sure the resulting messages are adequate, and clicks in the option to send the emails. One of the students received a message like the following:
|Cond 1:||Dear Osiri,
Quite a few students had to move lab classes the past two weeks. This is just to confirm that you are now in lab Group 18.
|Cond 2:||Good start with Physical Modeling! Make sure you review the exercises again.|
|Cond 6:||I noticed you are a keen participant of our lecture exercises. Do you know that they can be accessed before as well as after the lecture, not just during the lecture?|
|Cond 7:||You seem to have had problems with one of the forces questions. Please have a look at HRW Chapter 3.2.2 where this case is discussed in more detail.|
|Pilot:||Remember that the third homework assignment is due 11.00 pm Friday next week.
At the end of the semester he has a fairly comprehensive set of indicators, rules and personalised emails he plans to re-use for the next edition of the course, but perhaps with some modifications after hearing some comments from the students and suggestions from Alice.
Also at the end of the semester, Mike uses OnTask to explore possible relationships between the indicators captured during the course and the academic performance. He selects a few columns that he thinks are relevant, selects the columns with the scores and pushes a button that sends the data to a machine learning algorithm that groups students with similar indicators and the results are appended to the matrix. Mike reviews the results and detects some patterns for a group of students that require additional rules for the next semester.
Mike’s course has been systematically obtaining good results in the Student Evaluation Survey, but lately, with the use of OnTask he has noticed some improvements on how students perceive the provision of feedback, as well as academic attainment. These results have prompted the Dean’s commendation to Mike and Alice for their teaching. The learning and teaching support unit is now providing 2 hour workshops on the use of OnTask and want to encourage other academics and educational designers to use it.
Scenario 2: Using LMS data for personalised visualisations
Caleb is an adjunct professor and teaches a course on Wednesday nights about economics. He uses several videos and preparation questions each week as well as a discussion forum. He would like to know how are the students using those resources and give them some type of information about that level of engagement. He would like students to see their engagement with the videos, their scores in the formative assessment items in the LMS for the week, and their activity within the discussion forum. Caleb plans to use these visualisations to discuss in class ways to improve student participation.
Alice has connected to OnTask and with a single click has pre-populated a matrix with the information about the students enrolled in Caleb’s course. Together with Graham from ICT, they managed to upload data collected by the LMS about student access to resources automatically to OnTask through its API. The task required Graham to write a program that reads the database from the LMS and using OnTask’s API to upload a set of indicators in the matrix of Caleb’s course. Alice has given Graham a few suggestions about the best visualisations for the students and he has developed a new plug in OnTask that reads the indicators and generates a set of personalised visualisation for each student.
Caleb received an email before the beginning of the semester that contained the URL to access these visualisations and made it available to the students in the LMS. The data is automatically updated at the end of the day, so students can see their progress throughout the semester. During the semester Caleb has identified certain situations that prompted him to contact the students and ask them if they needed extra support. He did that within OnTask by first creating a set of if-this-then-that rules and several personalised texts, and then send emails to the correct students. Only a few students received the emails, but it helped Caleb quickly address some confusion about the course.
Alice has made sure that those rules that Caleb wrote can be used for the following edition of the course. She also shared them with another course and showed its instructor how to use the rules. She uploaded the list of students in this new course and after a few edits to match the new set of indicators in the conditions, they were ready to be used by another instructor.
Scenario 3: Seeing attendance, quizzes and consultations
Felicity, the coordinator of a large first year unit has contacted Alice to see if OnTask can be used for this semester. Alice connected to OnTask with her university credentials and showed Felicity the recently created matrix with the names and ids of her students. Felicity would like to see in OnTask the information related to each student when they come to consultation hours. With a few clicks she would like to see in a single screen the information related to a student while maintaining a consultation session.
Alice has explained the requirements to Graham from ICT. Graham has used the API available in OnTask and created a mobile application that using an API access key provided for every user in OnTask is capable of scanning a bar code in the student card and update the information in a previously identified column of the student matrix in Felicity’s course in OnTask. The tutors for Felicity’s course are using the app at the entrance of the lab sessions to log attendance. Using the same API, Graham also managed to propagate the midterm scores from the LMS to OnTask and create visualisations that show within OnTask the set of data collected for one student.
Felicity now has consultation hours and two students arrive. She asks them about their student ids, connects to OnTask and reflects the visit in their records by introducing or updating a value in one of the columns. She can also quickly glance through the attendance and assessment scores of both students because OnTask has been enhanced with a plug-in that makes available such information to the instructor.
Half way through the semester Felicity would like to provide a variety of resources to the students based on their engagement so far with the course. She writes a few rules and accompanying text with some additional resources. After she is happy with the content and the way the students are selected, she enables the action and posts the URL in the course forum for students to access those resources.
After using the tool for the first year Felicity has realised that there are various situations that are easy to predict with the information captured by the students. Furthermore, OnTask has kept a detailed record of the actions performed by the instructors as well as the times the students accessed their data. Reviewing this data Felicity now has solid evidence of the impact of these measures and for the next semester she plans to create a set of if-then-this-that rules for each week to customise the content of an email to send to students at the beginning of each week.
At the end of the semester, Felicity has also realised that it would be good to have the demographic information about her students as part of the report shown by OnTask. She talks to Sally (the DVC Education) to create a multidisciplinary group with Graham, Alice and Humai, the responsible for business intelligence and reporting at the university. They will work together to make sure the demographics data required by Felicity is available in OnTask for the next semester and identify some additional data sources for further integration.
Scenario 4: Students at risk and interaction with a CRM
Mathias coordinates a first year unit with a large enrolment that is core for a few programs. The delivery of the unit requires several procedures to be done extremely efficient; otherwise, they cannot be deployed at scale. The university has been working for the last few years to identify a model that successfully predicts when students are about to abandon this course or their studies. The model is based on a set of conditions stated in terms of indicators such as HSC scores, socioeconomic status, and several indicators of student engagement during the first weeks of the semester.
Humai, from the business intelligence unit has created such model. Together with Graham, from ICT, and as part of the institutional plan proposed by Sally (DVC Education) to deploy learning analytics to improve the student experience, they have devised a way to upload the data required by the model into OnTask. Graham will write a simple application that will use the API key provided by the tool to upload the data into the student matrix that Mathias will see in OnTask.
Alice, the educational designer helping Mathias, will write a set of if-this-then-that rules that combine the model created by Humai with the data collected in the LMS. Alice and Mathias have identified the three times during the semester that are best to execute the rules and detect these students at risk. Once a rule is activated for a student, OnTask will automatically send a request to the CRM platform already existing in the department of student support led by Zainab. She receives the information from OnTask and follows up with a variety of actions ranging from phone calls, SMS, emails, etc.
Zainab’s department runs a survey with the students asking them about their perception of the support. The results are very positive and Zainab writes a report that is sent to Sally (DVC Education) and highlights the effectiveness of using this tool to capture the know-how that Mathias has about his course, combine it with the model created by Humai and then provide truly personalised support to students. OnTask has kept track of the interventions so that they can be used as evidence to show the effect of the initiative.