Scholars have applied automatic content analysis to study computer-mediated communication in computer-supported collaborative learning (CSCL). Since CSCL also takes place in face-to-face interactions, we studied the automatic coding accuracy of manually transcribed face-to-face communication. We conducted our study in an authentic higher-education physics context where computer-supported collaborative inquiry-based learning (CSCIL) is a popular pedagogical approach. Since learners’ needs for support in CSCIL vary in the different inquiry phases (orientation, conceptualization, investigation, conclusion, and discussion), we studied, first, how the coding accuracy of five computational models (based on word embeddings and deep neural networks with attention layers) differed in the various inquiry-based learning (IBL) phases when compared to human coding. Second, we investigated how the different features of the best performing computational model improved the coding accuracy. The study indicated that the accuracy of the best performing computational model (differentiated attention with pre-trained static embeddings) was slightly better than that of the human coder (58.9% vs. 54.3%). We also found that considering the previous and following utterances, as well as the relative position of the utterance, improved the model’s accuracy. Our method illustrates how computational models can be trained for specific purposes (e.g., to code IBL phases) with small data sets by using pre-trained models.

Acoustic features and machine learning models have been recentlyproposed as promising tools to analyze lessons. Furthermore, acoustic patterns, both in the time and spectral domain, have been found to be related to teacher pedagogical practices. Nonetheless, most of previous work relies on expensive or third party equipment, limiting its scalability, and additionally, it is mainly used for diarization. Instead, in this work we present a cost-effective approach to identify teachers' practices according to three categories (Presenting, Administration, and Guiding) which are compiled from the Classroom Observation Protocol for Undergraduate STEM. Particularly, we record teachers' lessons using low-cost microphones connected to their smartphones. We then compute the mean and standard deviation of the amplitude, Mel spectrogram, and Mel Frequency Cepstral coefficients of the recordings to train supervised models for the task of predicting three categories compiled from the Classroom Observation Protocol for Undergraduate STEM. We found that spectral features perform better at the task of predicting teachers' activities along the lessons and that our models can predict the presence of the two most common teaching practices with over 80% of accuracy and good discriminative power. Finally, with these models, we found that using audio obtained from the teachers' smartphones it is also possible to automatically discriminate between sessions where students are using or not an online platform. This approach is important for teachers and other stakeholders who could use an automatic and cost-effective tool for analyzing teaching practices.

Computer-supported collaborative inquiry-based learning (CSCIL) represents a form of active learning in which students jointly pose questions and investigate them in technology-enhanced settings. Scaffolds can enhance CSCIL processes so that students can complete more challenging problems than they could without scaffolds. Scaffolding CSCIL, however, would optimally adapt to the needs of a specific context, group, and stage of the group's learning process. In CSCIL, the stage of the learning process can be characterized by the inquirybased learning (IBL) phase (orientation, conceptualization, investigation, conclusion, and discussion). In this presentation, we illustrate the potential of automatic content analysis to find the different IBL phases from authentic groups' face-to-face CSCIL processes to advance the adaptive scaffolding. We obtain vector representations from words using a well-known feature engineering technique called Word Embedding. Subsequently, the classification task is done by a neural network that incorporates an attention layer. The results presented in this work show that the proposed best performing model adds interpretability and achieves a 58.92% accuracy, which represents a 6% improvement compared to our previous work, which was based on topic-models.

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The emotions that students experience when engaging in tasks critically influence their performance and many models of learning and competence include assumptions about affective variables and respective emotions. However, while researchers agree about the importance of emotions for learning, it remains challenging to connect momentary affect, i.e., emotions, to learning processes. Advances in automated speech recognition and natural language processing (NLP) allow real time detection of emotions in recorded language. We use NLP and machine learning techniques to automatically extract information about students’ motivational states while engaging in the construction of explanations and investigate how this information can help more accurately predict students’ learning over the course of a 10-week energy unit. Our results show how NLP and ML techniques allow the use of different modalities of the same data in order to better understand individual differences in students’ performances. However, in realistic settings, this task remains far from trivial and requires extensive preprocessing of the data and the results need to be interpreted with care and caution. Thus, future research is needed before these methods can be deployed at scale.

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