Learning analytics are software algorithms that are used to predict or detect unknown aspects of the learning process, based on historical data and current behavior. There are four main categories of learning analytics:
* descriptive (what happened?)
* predictive (what will happen next?)
* diagnostic (why did it happen?)
* prescriptive (do this to improve)
==Overview==
Most commercial solutions are descriptive only. Those that are predictive or proactive make certain assumptions about learning that don't apply to everyone.
Beginning in version 3.4, Moodle core now implements open source, transparent next-generation learning analytics using machine learning backends that go beyond simple descriptive analytics to provide predictions of learner success, and ultimately diagnosis and prescriptions (advisements) to learners and teachers.
In Moodle 3.4, this system ships with two built-in models:
=== Analytics vs. reporting ===
Moodle provides a variety of built-in reports based on log data, but they are primarily descriptive in nature—they tell participants what happened, but not why, and they don't predict outcomes or advise participants how to improve outcomes. Log entries, while very detailed, are not in themselves descriptive of the learning process. They tell us “who,” “what,” and “when,” but not “why” or “how well.” Much more context is needed around each micro-action to develop a pattern of engagement.
* [[Students at risk of dropping out]]
Many third-party plugins also exist for Moodle that provide descriptive analytics. There are also integrations with third-party off-site reporting solutions. Again, these primarily provide descriptive analytics that rely on human judgment to interpret reports and generate predictions and prescriptions.
* No teaching activity
The system can be easily extended with new custom models, based on reusable targets, indicators, and other components. For more information, see the [[dev:Analytics API| Analytics API]] developer documentation.
Often in the past, learning analytics systems have attempted to analyze past activities to predict future activities in real time. With Moodle Learning Analytics, we are more ambitious. We believe a full learning analytics solution will help us not only predict events, but change them to be more positive.
===Features===
===Features===
* Two built-in prediction models: "[[Students at risk of dropping out]]" and "No Teaching".
* Two types of models supported:
* A set of student engagement indicators based on the [https://en.wikipedia.org/wiki/Community_of_inquiry Community of Inquiry].
** Machine-learning based models, including predictive models
** "Static" models to detect situations of concern using simple rules
* Three built-in models: "[[Students at risk of dropping out]]", "Upcoming activities due" and "No Teaching".
* A set of student engagement [[Learning analytics indicators|indicators]] based on the [https://en.wikipedia.org/wiki/Community_of_inquiry Community of Inquiry].
* Built-in tools to evaluate models against your site's data
* Built-in tools to evaluate models against your site's data
* Proactive notifications for instructors using Events
* Proactive notifications using [[Events list report|Events]]
* Instructors can easily send messages to students identified by the model, or jump to the Outline report for that student for more detail about student activity
* A list of suggested Actions is provided with the Insight notifications for each model. For example, in the [[Students at risk of dropping out]] model, instructors can easily send messages to students identified by the model, or jump to the [[Activity report]] for that student for more detail about student activity within the course
* An [[dev:Analytics API| API]] to build indicators and prediction models for third-party Moodle plugins
* An [[dev:Analytics API|API]] to build indicators and prediction models for third-party Moodle plugins
* Machine learning backend plugin type - supports PHP and Python, and can be extended to implement other ML backends
* Machine learning backend plugin type - supports PHP and Python, and can be extended to implement other ML backends
* The system can be easily extended with new custom models, based on reusable targets, indicators, and other components. For more information, see the [[dev:Analytics API|Analytics API]] developer documentation.
'''Note: PHP 7.x is required.'''
===Limitations===
===Limitations===
This release of Moodle Learning Analytics has the following limitations:
* Models included in this release must be "trained" on a site with previous completed courses, ideally using the Moodle course completion feature. The current models cannot make predictions on a site until this is done.
* Machine learning models such as [[Students at risk of dropping out]] must be trained on a site with data . These models cannot make predictions on a site until this is done.
* The prediction model included with this version requires that courses have fixed start and end dates, and is not designed to be used with rolling enrollment courses. Models that support a wider range of course types will be included in future versions of Moodle.
* Models must be designed and selected to match the educational priorities of the institution.
* Models and predictions are only visible to teachers and administrators at present.
We are continuing to enhance Moodle Learning Analytics, and expanded capabilities will be released going forward. To help contribute to our progress, please join the conversation at the [https://moodle.org/project_inspire Moodle Learning Analytics Community]. In particular, we still need data sets from a wide variety of Moodle-using institutions in order to be able to ship a working prediction model that does not depend on local site data before it can be used.
== Settings ==
== Settings ==
You can access ''Analytics settings'' from ''Site administration > Analytics > Analytics settings''.
The Moodle learning analytics system requires some initial configuration before use. See [[Analytics settings]] for more detail.
Prediction processors are the machine learning backends that process the datasets generated from the calculated indicators and targets and return predictions. Moodle core includes 2 prediction processors:
* The PHP processor is the default. There are no other system requirements to use this processor.
* The Python one is more powerful and it generates [https://www.tensorflow.org/get_started/summaries_and_tensorboard graphs that explain the model performance]. It requires setting up extra tools: Python itself (https://wiki.python.org/moin/BeginnersGuide/Download) and the moodlemlbackend python package.
pip install moodlemlbackend
=== Time splitting methods ===
[[dev:Analytics API#Time splitting methods|Time splitting methods]] allow insights generated from one course to be used on another course, even if the two courses are not exactly the same length.
Each time splitting method divides the course duration into segments. At the end of each defined segment, the predictions engine will run and generate insights. It is recommended that you only enable the time splitting methods you are interested in using; the evaluation process will iterate through all enabled time-spitting methods, so the more time-splitting methods enabled, the slower the evaluation process will be.
This setting allows you to define a directory where machine learning backends data is stored. Be sure this directory exists and is writable by the web server.
== Model management ==
Moodle can support multiple prediction models at once, even within the same course. This can be used for A/B testing to compare the performance and accuracy of multiple models.
Moodle core ships with two prediction models, ''[[Students at risk of dropping out]]'' and ''No teaching.'' Additional prediction models can be created by using the [[dev:Analytics API| Analytics API]]. Each model is based on the prediction of a single, specific "target," or outcome (whether desirable or undesirable), based on a number of selected indicators.
You can manage your system models from ''Site Administration > Analytics > Analytics models''.
[[File:prediction-models-list.jpeg]]
These are some of the actions you can perform on a model:
* '''Get predictions:''' Train machine learning algorithms with the new data available on the system and get predictions for ongoing courses. ''Predictions are not limited to ongoing courses-- this depends on the model.''
* '''Evaluate:''' (disabled by default) Evaluate the prediction model by getting all the training data available on the site, calculating all the indicators and the target and passing the resulting dataset to machine learning backends. This process will split the dataset into training data and testing data and calculate its accuracy. Note that the evaluation process uses all information available on the site, even if it is very old. Because of this, the accuracy returned by the evaluation process may be lower than the real model accuracy as indicators are more reliably calculated immediately after training data is available because the site state changes over time. The metric used to describe accuracy is the ''[https://en.wikipedia.org/wiki/Matthews_correlation_coefficient Matthews correlation coefficient]'' (a metric used in machine learning for evaluating binary classifications)
* '''Log:''' View previous evaluation logs, including the model accuracy as well as other technical information generated by the machine learning backends like ROC curves, learning curve graphs, the tensorboard log dir or the model's Matthews correlation coefficient. The information available will depend on the machine learning backend in use.
* '''Edit:''' You can edit the models by modifying the list of indicators or the time-splitting method. All previous predictions will be deleted when a model is modified. Models based on assumptions (static models) can not be edited.
* '''Enable / Disable:''' The scheduled task that trains machine learning algorithms with the new data available on the system and gets predictions for ongoing courses skips disabled models. Previous predictions generated by disabled models are not available until the model is enabled again.
* '''Export''': Export your site training data to share it with your partner institutions or to use it on a new site.
* '''Invalid site elements''': Reports on what elements in your site can not be analysed by this model
* '''Clear predictions''': Clears all the model predictions and training data
[[File:model-evaluation.jpeg]]
You can force the model evaluation process to run from the command line:
$ admin/tool/analytics/cli/evaluate_model.php
=== Core models ===
==== Students at risk of dropping out ====
[[Students at risk of dropping out|This model]] predicts students who are at risk of non-completion (dropping out) of a Moodle course, based on low student engagement. In this model, the definition of "dropping out" is "no student activity in the final quarter of the course." The prediction model uses the [https://en.wikipedia.org/wiki/Community_of_inquiry Community of Inquiry] model of student engagement, consisting of three parts:
* [[Students at risk of dropping out#Cognitive depth|Cognitive presence]]
* [[Students at risk of dropping out#Social Breadth|Social presence]]
* [[Students at risk of dropping out#Potential indicator levels for selected activity modules|Teacher presence]]
This prediction model is able to analyse and draw conclusions from a wide variety of courses, and apply those conclusions to make predictions about new courses. The model is not limited to making predictions about student success in exact duplicates of courses offered in the past. However, there are some limitations:
# This model requires a certain amount of in-Moodle data with which to make predictions. At the present time, only core Moodle activities are included in the [[Students at risk of dropping out#Indicators|indicator]] set (see below). Courses which do not include several core Moodle activities per “time slice” (depending on the time splitting method) will have poor predictive support in this model. This prediction model will be most effective with fully online or “hybrid” or “blended” courses with substantial online components.
# This prediction model assumes that courses have fixed start and end dates, and is not designed to be used with rolling enrollment courses. Models that support a wider range of course types will be included in future versions of Moodle. Because of this model design assumption, it is very important to properly set course start and end dates for each course to use this model. If both past courses and ongoing courses start and end dates are not properly set predictions cannot be accurate. Because the course end date field was only introduced in Moodle 3.2 and some courses may not have set a course start date in the past, we include a command line interface script:
This script attempts to estimate past course start and end dates by looking at the student enrolments and students' activity logs. After running this script, please check that the estimated start and end dates script results are reasonably correct.
==== No teaching ====
This model's insights will inform site managers of which courses with an upcoming start date will not have teaching activity. This is a simple model and it does not use machine learning backend to return predictions. It bases the predictions on assumptions, e.g. there is no teaching if there are no students.
== Predictions and Insights ==
Models will start generating predictions at different point in time, depending on the site prediction models and the site courses start and end dates.
Each model defines which predictions will generate [[dev:Analytics API#Insights|insights]] and which predictions will be ignored. For example, the ''[[Students at risk of dropping out]]'' prediction model does not generate an insight if a student is predicted as "not at risk," since the primary interest is which students are at risk of dropping out of courses, not which students are not at risk.
[[File:prediction-model-insights.jpeg]]
== Using analytics ==
The Moodle Learning Analytics API is an open system that can become the basis for a very wide variety of models. Models can contain indicators (a.k.a. predictors), targets (the outcome we are trying to predict), insights (the predictions themselves), notifications (messages sent as a result of insights), and actions (offered to recipients of messages, which can become indicators in turn).
=== Actions===
Most learning analytics models are not enabled by default. Enabling models for use should be done after considering the institutional goals the models are meant to support. See [[Using analytics]] for more information.
Each insight can have one or more actions defined. Actions provide a way to act on the insight as it is read. These actions may include a way to send a message to another user, a link to a report providing information about the sample the prediction has been generated for (e.g. a report for an existing student), or a way to view the details of the model prediction.
Analytics models may be restricted to category or course contexts.
[[Image:08_insights.png|frame|center|Insights and Actions]]
== Managing models ==
Once models have been enabled and trained, insights will be generated. Models should also be monitored for performance and accuracy. See [[Managing analytics]] for more information.
Insights can also offer two important general actions that are applicable to all insights. First, the user can acknowledge the insight. This removes that particular prediction from the view of the user, e.g. a notification about a particular student at risk is removed from the display.
==See also==
The second general action is to mark the insight as "Not useful." This also removes the insight associated with this calculation from the display, but the model is adjusted to make this prediction less likely in the future.
Learning analytics are software algorithms that are used to predict or detect unknown aspects of the learning process, based on historical data and current behavior. There are four main categories of learning analytics:
descriptive (what happened?)
predictive (what will happen next?)
diagnostic (why did it happen?)
prescriptive (do this to improve)
Most commercial solutions are descriptive only. Those that are predictive or proactive make certain assumptions about learning that don't apply to everyone.
Analytics vs. reporting
Moodle provides a variety of built-in reports based on log data, but they are primarily descriptive in nature—they tell participants what happened, but not why, and they don't predict outcomes or advise participants how to improve outcomes. Log entries, while very detailed, are not in themselves descriptive of the learning process. They tell us “who,” “what,” and “when,” but not “why” or “how well.” Much more context is needed around each micro-action to develop a pattern of engagement.
Many third-party plugins also exist for Moodle that provide descriptive analytics. There are also integrations with third-party off-site reporting solutions. Again, these primarily provide descriptive analytics that rely on human judgment to interpret reports and generate predictions and prescriptions.
Often in the past, learning analytics systems have attempted to analyze past activities to predict future activities in real time. With Moodle Learning Analytics, we are more ambitious. We believe a full learning analytics solution will help us not only predict events, but change them to be more positive.
Features
Two types of models supported:
Machine-learning based models, including predictive models
"Static" models to detect situations of concern using simple rules
A list of suggested Actions is provided with the Insight notifications for each model. For example, in the Students at risk of dropping out model, instructors can easily send messages to students identified by the model, or jump to the Activity report for that student for more detail about student activity within the course
An API to build indicators and prediction models for third-party Moodle plugins
Machine learning backend plugin type - supports PHP and Python, and can be extended to implement other ML backends
The system can be easily extended with new custom models, based on reusable targets, indicators, and other components. For more information, see the Analytics API developer documentation.
Limitations
Machine learning models such as Students at risk of dropping out must be trained on a site with data . These models cannot make predictions on a site until this is done.
Models must be designed and selected to match the educational priorities of the institution.
Settings
The Moodle learning analytics system requires some initial configuration before use. See Analytics settings for more detail.
Using analytics
The Moodle Learning Analytics API is an open system that can become the basis for a very wide variety of models. Models can contain indicators (a.k.a. predictors), targets (the outcome we are trying to predict), insights (the predictions themselves), notifications (messages sent as a result of insights), and actions (offered to recipients of messages, which can become indicators in turn).
Most learning analytics models are not enabled by default. Enabling models for use should be done after considering the institutional goals the models are meant to support. See Using analytics for more information.
Analytics models may be restricted to category or course contexts.
Managing models
Once models have been enabled and trained, insights will be generated. Models should also be monitored for performance and accuracy. See Managing analytics for more information.
