Advances in Learning Analytics are expected to contribute new empirical findings, theories, methods, and metrics for understanding how students learn. It also could contribute to improving pedagogical support for students’ learning through assessment of new digital tools, teaching strategies, and curricula.
The most recent direction within this area is Multimodal Learning Analytics, which emphasizes the analysis of natural rich modalities of communication during situated learning activities. This includes students’ speech, writing, and nonverbal interaction (e.g., gestures, facial expressions, gaze, etc.). A primary objective of multimodal learning analytics is to analyze coherent signal, activity, and lexical patterns to understand the learning process and feedback its participants in order to improve it. The Third International Workshop on Multimodal Learning Analytics will bring together international researchers in multimodal interaction and systems, cognitive and learning sciences, educational technologies, and related areas to advance research on multimodal learning analytics.
Following the First International Workshop on Multimodal Learning Analytics in Santa Monica in 2012 and the ICMI Grand Challenge on Multimodal Learning Analytics in Sydney in 2013, this third workshop will also incorporate two data-driven grand challenges. It will be held at ICMI 2014 in Istanbul, Turkey on November 12th 2014. This year, the workshop has been expanded to include a session for hand-on training on multimodal learning analytic techniques and two dataset-based challenges. Students and postdoctoral researchers are especially welcome to participate.
- March 24, 2014: Both datasets are made available to interested participants
- July 15, 2014: Deadline for workshop papers
- August 8, 2014: Deadline for grand challenge papers
- August 21, 2014 Notification of acceptance
- September 15, 2014: Camera ready papers due
- November 12, 2014: Workshop event
The workshop will focus on the presentation of multimodal signal analysis techniques that could be applied in Multimodal Learning Analytics. Instead of requiring research results, that usually are presented at the Learning Analytics and Knowledge (LAK) or Multidimodal Interaction (ICMI) conferences, this event will require presenters to concentrate on benefits and shortcomings of methods used for multimodal analysis of learning signals.
Following the successful experience of the Multimodal Learning Analytics Grand Challenge in ICMI 2013, this year this event will provide two data sets with diverse research questions to be tackled by interested participants:
The Math Data Corpus (Oviatt, 2013) is available for analysis. It involves 12 sessions, with small groups of three students collaborating while solving mathematics problems (i.e., geometry, algebra). Data were collected on their natural multimodal communication and activity patterns during these problem-solving and peer tutoring sessions, including students’ speech, digital pen input, facial expressions, and physical movements. In total, approximately 1518 hours of multimodal data is available during these situated problemsolving sessions. Participants were 18 high-school students, including 3-person male and female groups. Each group of three students met for two sessions. These student groups varied in performance characteristics, with some low-to-moderate performers and others high-performing students. During the sessions, students were engaged in authentic problem solving and peer tutoring as they worked on 16 mathematics problems, four apiece representing easy, moderate, hard,￼￼ and very hard difficulty levels. Each problem had a canonical correct answer. Students were motivated to solve problems correctly, because one student was randomly called upon to explain the answer after solving it. During each session, natural multimodal data were captured from 12 independent audio, visual, and pen signal streams. Software was developed for accurate time synchronization of all twelve of these media streams during collection and playback. The data have been segmented by start and end time of each problem, scored for solution correctness, and also scored for which student solved the problem correctly. This corpus used for the ICMI Grand Challenge on Multimodal Learning Analytics in 2013. This dataset has been expanded with full manual and automatic transcripts of the students speech. It also contains more than 10.000 annotations of the students diagrams during problem solving. The main research questions behind this dataset will be automatic prediction of which math problems will be solved correctly or not, which student in a group is the dominant domain expert, and identification of significant precursors of performance and learning. Predictors could be based on information from unimodal or multimodal signals, lexical/representational content, individual or group dynamics, or combined information sources.
This challenge includes a data corpus that involves 40 oral presentations of Spanish-speaking students in groups of 4 to 5 members presenting projects (entrepreneurship ideas, literature reviews, research designs, software design, etc.). Data were collected on their natural multimodal communication in regular classroom settings. The following data is available: speech, facial expressions and physical movements in video, skeletal data gathered from Kinect for each individual, and slide presentation files. In total, approximately 10 hours of multimodal data is available for analysis of these presentations. In addition grading for individuals when doing their presentations is included as well as a group-grade related to the quality of the slides used when doing each presentation.
This challenge seeks to solve the following questions:
a) How multimodal techniques can help us in evaluating presentation skills when doing presentations and
b) How good is a group presentation based on the individual presentations and the quality of the slides used in a presentation.
This corpus has been recorded and will be made available at the end of March.
- Xavier Ochoa, ESPOL, Ecuador (email@example.com)
- Marcelo Worsley, Stanford, USA (firstname.lastname@example.org)
- Katherine Chiluiza, ESPOL, Ecuador (email@example.com)
- Saturnino Luz, Trinity College Dublin, Ireland (firstname.lastname@example.org)