Teilnehmer/innen, die Gefahr laufen, aus dem Kurs auszusteigen

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Überblick

Ab Version 3.4 implementiert Moodle auf Open Source Basis eine Learning Analytics Funktionalität. Moodle stellt im Standardpaket ein Modell Teilnehmer/innen sind gefährdet auszusteigen bereit. Dieses Modell wird im Folgenden detailliert beschrieben.

Das Modell generiert Vorhersagen darüber, welche Teilnehmer/innen gefährdet sind, vorzeitig aus einem Moodle-Kurs auszusteigen (drop-out). "Vorzeitig aussteigen" bedeutet in diesem Zusammenhang, dass eine Person im letzten (zeitlichen) Viertel des Kurses keine Aktivitäten im Kurs mehr zeigt. Dieses Vorhersagemodell verwendet das Community of Inquiry Modell, welches auf dem Engagement der Kursteilnehmer/innen basiert und auf drei Konzepten beruht:

Weiter unten wird beschrieben, wie diese Konzepte im Modell definiert sind.

Funktionalitäten

Durch Abstraktion der Konzepte "Kognitive Präsenz" und "Soziale Präsenz" ist das Vorhersagemodell in der Lage, eine Vielzahl von Kursen zu analysieren und daraus Schlussfolgerungen zu ziehen. Auf Basis der Schlussfolgerungen macht das Modell Vorhersagen für neue Kurse, sogar für Kurse, in denen bisher noch kein Unterricht bzw. keine Lehrtätigkeit stattgefunden hat. Damit beschränken sich die Vorhersagen des Modells nicht auf den Lernerfolg der Teilnehmer/innen in Kursen, die exakte Kopien von Kursen aus der Vergangenheit sind.

Einschränkungen

  • Das Vorhersagemodell setzt voraus, dass Kurse ein festes Kursbeginndatum und ein festes Kursendedatum haben. Das Modell funktioniert nicht bei Kursen mit fortlaufenden Einschreibungen. Modelle ohne diese Einschränkungen sollen in zukünftigen Moodle-Versionen verfügbar sein.
    • Wegen dieser Einschränkung ist es sehr wichtig, für alle Kurse, die das Modell verwenden, die Daten von Kursbeginn und Kursende richtig zu setzen. Wenn dies für vergangene und laufende Kurse nicht der Fall ist, dann können die Vorhersagen nicht verlässlich sein.
    • Es werden keine Kurse für das Training des Modells oder für Vorhersagen verwendet, in denen (fälschlicherweise) das Datum des Kursendes vor dem Datum des Kursbeginns liegt.
  • Das Modell erfordert es, dass innerhalb des Kurses Abschnitte verwendet werden, damit die einzelnen Aktivitäten auf Zeitabschnitte aufgeteilt werden können.

Die Funktionalitäten von Moodle Analytics haben folgende Einschränkungen:

  • Es werden keine Kurse verwendet, deren Beginn und Ende mehr als ein Jahr zurückliegen.
  • Das Modell erfordert eine gewisse Menge an Moodle-internen Daten, um darauf basierend Vorhersagen zu machen. Zum gegenwärtigen Zeitpunkt werden nur Aktivitäten, die zum Standardpaket von Moodle gehören, in die Indikatoren einbezogen (siehe unten). Kurse, die pro Zeitabschnitt nur wenige Standard-Aktivitäten enthalten, werden zu schwachen Vorhersagen führen. Das Modell ist am effektivsten, wenn die Kurse substantielle Online-Komponenten enthalten.

Da die Kurseinstellung Kursende erst ab Moodle 3.2 verfügbar war und eine Kurse in der Vergangenheit möglicherweise kein Kursbeginndatum gesetzt hatten, stellen wir ein Kommandozeilen-Tool zur Verfügung:

$ admin/tool/analytics/cli/guess_course_start_and_end.php

Dieses Skript versucht, aus den Einschreibungen der Teilnehmer/innen und den Aktivitäten-Logdaten die Daten von Kursbeginn und Kursende zu schätzen. Wenn Sie das Skript laufen lassen, prüfen Sie bitte anschließend, ob die so bestimmten Daten von Kursbeginn und Kursende hinreichend passen.

Ziel

The target here is "students drop out of the course" (negative target) and is defined as: Enroled students show no activity in the final quarter of the course.

Additionally,

  1. Enrolments in which the time end is before the current start date in the course will be excluded from predictions.
  2. Enrolments lasting more than 1 year are excluded
  3. Course completion can be used as a success metric if it is enabled
  4. Otherwise, activity within the last quarter of the course is considered "not dropping out"

The code for this target is located at moodlesite/opt/app/lib/classes/analytics/target/course_dropout.php

where moodlesite is the root directory of your moodle site.

Indicators

Indicators can be defined at any context level. The indicators used in this model are based on the concepts of "cognitive depth" and "social breadth," which are implemented for each of the core activity modules.

Kognitive Tiefe

Cognitive depth is a measure of the construct "cognitive presence" within the Community of Inquiry theoretical framework. Cognitive presence is defined as “The extent to which the participants in any particular configuration of a community of inquiry are able to construct meaning through sustained communication” (Garrison, Anderson & Archer, 2000, p 89). Cognitive presence has usually been determined in research by manual content analysis. In this model, we define this construct based on the type of activity offered to the student, and the extent to which the student demonstrates cognitive engagement in that activity. The level of depth ranges from 0 to 5, where 0 indicates that the learner has not even viewed the activity. The levels of potential cognitive depth are:

  1. The learner has viewed the activity details
  2. The learner has submitted content to the activity
  3. The learner has viewed feedback from an instructor or peer for the activity
  4. The learner has provided feedback to the instructor or a peer within the activity
  5. The learner has revised and/or resubmitted content to the activity

This model begins by assigning a maximum potential value of cognitive depth to each activity module. For example, the Assignment module allows up to cognitive depth of 4. See below for more details of how these levels are assigned for core activity modules.

Once the potential levels are assigned, each student enroled in a course is evaluated based on the proportion of the potential depth reached. For example, if an activity only supports up to level 3 and the student has reached level 3, the student is participating at 100 percent of the possible level of cognitive depth.

Soziale Breite

Social breadth is a measure of the construct "social presence" within the Community of Inquiry theoretical framework. It is defined as “The ability of participants to identify with the group or course of study, communicate purposefully in a trusting environment, and develop personal and affective relationships progressively by way of projecting their individual personalities” (Garrison, 2009, p 352). In the past, social presence has usually been measured via post-course surveys and manual discourse analysis, though there have been increasing attempts to automate this process. This model implements social presence as "social breadth" by examining the breadth of opportunities the participant has to communicate with others. The level of breadth ranges from 0 to 5, where 0 indicates the learner has not interacted with anyone. The levels of potential social breadth are:

  1. The learner has not interacted with any other participant in this activity (e.g. they have read a page)
  2. The learner has interacted with at least one other participant (e.g. they have submitted an assignment or attempted a self-grading quiz providing feedback)
  3. The learner has interacted with multiple participants in this activity, e.g. posting to a discussion forum, wiki, database, etc.
  4. The learner has interacted with participants in at least one "volley" of communications back and forth
  5. The learner has interacted with people outside the class, e.g. in an authentic community of practice

This model begins by assigning a maximum potential value of social breadth to each activity module. For example, the Assignment module allows up to social breadth of 2. See below for more details of how these levels are assigned for core activity modules.

Once the potential levels are assigned, each student enroled in a course is evaluated based on the proportion of the potential depth reached. For example, if an activity only supports up to level 3 and the student has reached level 3, the student is participating at 100 percent of the possible level of social breadth.

Potentielle Indikatoren für ausgewählte Aktivitäten

The potential for engagement via cognitive presence and social presence constitutes instructional design, which is one key element of teaching presence. This diagram shows the potential cognitive depth and social breadth of all core activities and select non-core activities:

Datei:07 depth breadth.png
Potential cognitive depth and social breadth

By categorizing each activity by potential cognitive depth and social breadth, we can anticipate what level of engagement is supported (and possibly expected) of the learner, even without a history of many learners’ actions in that activity instance. Note that higher levels along each axis include all lower levels, i.e. an activity that involves a student and all peers (social breadth 3) automatically includes levels 1 (student only) and 2 (student +1). In many cases, the specific level can only be determined by analyzing the parameter settings for the activity. (Note that the model included in Moodle 3.4 only supports activities in Moodle Core. Some non-Core activities are included here as examples.)

Analysierbare Elemente

The "analysable" element of Moodle for this model is the Course. This indicates that the model will iterate through the courses on the site and process each one, either to train the model or to make predictions. Predictions are made for each "sample" entity (see below) within the context of the course.

For scalability reasons all calculations at course level are executed in per-course basis and the resulting datasets are merged together once all site courses analysis is complete.

Stichproben

"Samples" in the context of machine learning indicate the unit of analysis. In this model, the samples are student enrolments in courses. Predictions will be made for each student enrolment in a course, based on the data observed during the training of the model for all previous student enrolments in courses that have ended.

Gültige Stichproben

Valid samples are defined for each model in terms of model training and model predictions. For this model, the criteria are:

  • For prediction = ongoing courses
  • For training = finished courses with activity

Einschätzungen

Insights are the specific predictions generated by a model for each unit defined in the sample (in this case, student enrolments in a course) within the context of that model (in this case, each course). The context is used to define who will receive notifications based on the moodle/analytics:listinsights capability for that context. For this model, this permission is defined for the teacher role by default.

In this model, the insights are binary, i.e. "student at risk of dropping out" or "student not at risk of dropping out."

Actions

Each insight can have one or more actions defined. For this model, the actions are:

  • Send a message to the student
  • View the Outline report for the student in this course
  • View prediction details
  • Acknowledge the notification
  • Mark the notification as "not useful"


Insights and Actions

Empfohlene Zeitaufteilungsmethoden

The time splitting method is selected when the model is enabled. This depends on the typical length of courses and the length of the add/drop period (if relevant). If you wish to see predictions within the first two weeks of a 16 week course, you will need to use "tenths." (16 weeks = 112 days, so predictions will be calculated approximately every 11 days.) However, if you wish to see predictions every two weeks in an 8 week course, "quarters" will suffice. Remember, 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 each time it runs (and the longer the model will take to train).