ey are simple to read and are comprehensible by a wide audience. Colour of the charts categorises competencies into three levels: red demonstrates inadequate competency in a topic, yellow demonstrates adequate competency with room for improvement, and blue demonstrates mastery in a topic. Radar charts are visually striking, and can add interest to what would otherwise be a dry data presentation. One of the strengths of radar charts is that they support visualisation of multiple variables consisting of measures that require different quantitative scales, which a bar chart cannot accommodate. They are very well suited for comparing the knowledge states of users students. Box plots are an effective way of displaying the distribution of data based on minimum, median, maximum, 1st quartile and 3rd quartile. Box plot displays may be preferred when displaying data of a group of learners as they determine if the data is skewed based on where the median sits within the box relative to the inter-quartile ranges. However, they are harder than both bar charts and radar charts to read and comprehend. The widget also supports the use of more recently developed visualisations that are tailored towards education. For example, it supports the use of Topic Dependency Models[Cooper and Khosravi (2018)], which use two-weighted graphs to display the knowledge state of learners not only on based on individual topics but also on combination of two or more topics.
“Compare Data” allows learners to compare their knowledge states against a range of options: “Peers” mode enables them to compare their performance with a selected distribution (e.g. top 20%) of the peers that are currently enrolled in the course. ”Previous Offerings” enables learners to compare their performance with a selected distribution of learners across all offerings of the course. “Topic to Visualise” option enables users to select topics in which their competencies are visualised.
The bottom section of this page, as shown in Figure 4, enables learners to select questions using search and recommendation functionalities. The “Sort By” option allows learners to sort questions based on their difficulty, quality, number of responses, number of comments or personalised rating. By selecting ”Personalised Rating”, the platform sorts the questions based on the outcome of recommender system. The ”Filter” option enables users to filter the questions that are included in the results. They can request all questions (default), unanswered questions, answered questions, or wrongly answered questions to be included in the results. The “Search” option enables learners to search for questions based on specific content that may be present in the questions or multiple choice answers.
The results of the search are presented as a list of question cards, allowing users to engage with questions that best suit their needs. Figure 5 shows a sample question card. Each question card includes an overview of the question content, the topics associated with the question, and a sidebar in which the first icon shows the number of responses to the question, the second icon shows the average difficulty rating of the question, the third icon shows the average quality rating of the question, and the last icon shows the personalised rating that demonstrates the suitability of the question for each learner. Clicking on the question card will take the users to another page that would allow them to answer and rate the question.
4.3 Reciprocal Peer Recommendation
One of the main features of RiPPLE is its ability to recommend peer learning sessions. Learners nominate their availability in hourly blocks, and their preferences for providing or seeking peer learning support and finding study partners across the range of course-relevant topics. Figure 6 shows the graphical interface used for capturing this information in RiPPLE.
The shade around each of the time slots provides an indication of the popularity of that time, where darker shades indicate a higher amount of interest in the time slot. The knowledge state of the 