How can you identify user behavior patterns in an online learning platform?

Identifying user behavior patterns in an online learning platform involves leveraging analytics tools to track metrics like course completion rates, time spent on different modules, and frequency of interactions. Analyzing clickstreams and user navigation paths helps discern preferred content, while gathering feedback through surveys or quizzes aids in understanding user engagement and preferences, enabling a comprehensive overview of user behaviors within the platform.

Before we start looking at data, let's decide what we want to achieve. Do we want more people to stick around, finish what they started, be happy with the experience, or learn a lot? Once we figure that out, we pick simple things to measure, like how much time people spend, what pages they visit, what they do, or how well they do in quizzes. It's a bit like planning a trip. First, decide where you want to go (goals), and then pick tools to help you get there (metrics).

A few tools that the teams I have worked with used: Quantitative: -Web Analytics (Google Analytics): Track interactions. -Surveys (SurveyMonkey): Get feedback. -A/B Testing (Optimizely): Compare features. -Heatmaps (Hotjar): Visualize clicks. -Learning AI Analytics (IBM Watson): Analyze outcomes. -Session Recording (FullStory): Monitor navigation. -Device Analytics (Google Analytics): Study usage patterns. Qualitative: -Interviews (Zoom): In-depth insights. -AI Forum Analysis (Dovetail): Understand discussions. -Usability Testing (UserTesting): Find usability issues. -Feedback Widgets (Qualaroo): Instant user feedback. -Observational Studies (GoToMeeting): Observe behaviours. -Focus Groups (Teams): Gather diverse opinions.

There are various ways to collect user data. There are 2 main methods: Quantitive Methods - gives us the big picture and the majority behavior of our users. I use analytic tools such as Fullstory and Mixpanel. Qualitative methods - that drill down to a more focused understanding and good quality information. I use user interviews, watch user sessions in my apps. In addition, in the era of the advanced AI tools that available for us, I found a way to combine Qualitative & Quantitive research methods and bring them together for the first time. For example, we can take hundreds of calls with clients, and ask AI to go over them and briefly highlight the stuff stuff that relevant for us as UX designers.

I find having the why with the how in close proximity and association are what drive optimal data for the best understanding of user experiences. Humans use patterns that correspond through the behavior of trial and error. We shouldn't want to assume what they are doing in this phase.

1. Use data visualization tools: Create charts, graphs, and heatmaps to identify patterns in user behavior. 2. Segment users based on different variables: Analyze behavior based on demographics, learning styles, course choices, and performance. 3. Employ statistical analysis: Use hypothesis testing and correlation analysis to draw meaningful conclusions from the data.

Process Mining techniques can be applied to analyze the user journey on learning platforms. This approach provides a holistic comprehension of user experiences, empowering data-driven insights to enhance user satisfaction, optimize processes, and improve overall platform performance

I like descriptive data sets. From behavior to reasoning to objectives. Not in order. People are different and have different objectives for using a site. Understanding the why and how and aligning with the goal is always a good place to start. I like associative group analysis.

I like using path elements from user journeys which allow me to validate and summarize data set types. All of this work is solely from data assessment leaving out any biases.

In my experience, validating user data is like polishing a gem. Once, in a project, cross-validating insights from surveys and analytics ensured our strategies hit the mark. It's a safeguard for reliable, accurate decisions—key in the dynamic realm of online learning.

One of the most successful approaches I've used for sharing insights and making them "stick" is to use short-form video. This would include short snippets of user interviews or testing sessions, with key quotes, pain points or findings. What this does is generate empathy directly with the user in a way that a slide could never do. Assigning the user a name (doesn't have to be real) means that teams can then reference that particular user when working on JTBD and generally iterating on their products. Videos can be anywhere between 2-5 min long but not longer, to ensure people's max. attention is leveraged and to enable teams to refer back to these insights with ease.

I like to start off with the outcome first in my presentation slides and move into key points of validation from both user experiences and data assessments to reveal what is next. Outline key takeaways.

In my experience, telling a story with data is key. I usually craft reports and dashboards, to make numbers more vivid. Whether with presentations or feedback forms, keep it clear. It's like sharing a compelling story—captivating and actionable.

After analyzing relevant data and discussing on the results from both the in-situ observations and the purely digital database, we can profile several main tendencies and compare them to your original definitions and goals. This allows the quantitative to fusion with the qualitative in order for the data to guide the design toward improving the framework. The results must be discussed and presented in such a way that it allows for non-equivocal decision making. Whatever is ambiguous can be treated later with several design thinking sessions.

I think collecting both quantitative and qualitative data will give you a more concrete answer, however to get a deep understanding of the user's behaviours and the why behind those behaviours, nothing beats observing it in the real environment where it happens or in short, a contextual inquiry.

Always keep in mind that users are not "you and your team of experts". It's important to be "affordable", meaning that a 12 years old person should be able to find their way easily through your e-learning site, and so should a 60 year old person. Pure data will bring a huge amount of answers, but they can't replace human to human observations. e.g.: Data might show that the user is following the path intended and finally reaches the goal you wanted... But! Digital Data can't tell you if that user did swear all along the journey, felt washed-out by the experience, felt stressed and had to be particularly focused to try and understand what your UI/UX meant or wanted her to do. Heuristic evaluations just can be replaced by data mining.