Learning analytics to identify exploratory dialogue within synchronous text chat

While generic web analytics tend to focus on easily harvested quantitative data, Learning Analytics will often seek qualitative understanding of the context and meaning of this information. This is critical in the case of dialogue, which may be employed to share knowledge and jointly construct understandings, but which also involves many superficial exchanges. Previous studies have validated a particular pattern of “exploratory dialogue” in learning environments to signify sharing, challenge, evaluation and careful consideration by participants. This study investigates the use of sociocultural discourse analysis to analyse synchronous text chat during an online conference. Key words and phrases indicative of exploratory dialogue were identified in these exchanges, and peaks of exploratory dialogue were associated with periods set aside for discussion and keynote speakers. Fewer individuals posted at these times, but meaningful discussion outweighed trivial exchanges. If further analysis confirms the validity of these markers as learning analytics, they could be used by recommendation engines to support learners and teachers in locating dialogue exchanges where deeper learning appears to be taking place

Analysing video and audio data: existing approaches and new innovations

Across many subject disciplines, video and audio data are recorded in order to document processes, procedures or interactions. These video and audio data are consequently analysed using a number of techniques, in order to try and make sense of what was happening at the time of the recording, sometimes in relation to initial hypotheses or sometimes in terms of a 'post hoc' analysis where a more grounded approach is used. This paper contains an overview of tools and techniques for examining video data and looks at potential new methods borrowed from the field of learning analytics, related to discourse analysis. Discourse analysis, where conversations and the spoken word are explored and dissected in detail, can provide us with information about the learning context and the ways in which learners interact with people and other resources in their environment

An evaluation of learning analytics to identify exploratory dialogue in online discussions

Social learning analytics are concerned with the process of knowledge construction as learners build knowledge together in their social and cultural environments. One of the most important tools employed during this process is language. In this paper we take exploratory dialogue, a joint form of co-reasoning, to be an external indicator that learning is taking place. Using techniques developed within the field of computational linguistics, we build on previous work using cue phrases to identify exploratory dialogue within online discussion. Automatic detection of this type of dialogue is framed as a binary classification task that labels each contribution to an online discussion as exploratory or non-exploratory. We describe the development of a self-training framework that employs discourse features and topical features for classification by integrating both cue-phrase matching and k-nearest neighbour classification. Experiments with a corpus constructed from the archive of a two-day online conference show that our proposed framework outperforms other approaches. A classifier developed using the self-training framework is able to make useful distinctions between the learning dialogue taking place at different times within an online conference as well as between the contributions of individual participants

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers

Assessing the language of chat for teamwork dialogue

In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study will evaluate and explain preprocessing and classification methods used to analyze teamwork dialogue from a dataset of chat data. Analytics methods evaluated in this study provide a direction for assessing the language of chat for teamwork dialogue and can help extend the work of technology enhanced language learning to n ot only focus on academic competency, but on the communication aspect too

Discourse, computation and context – sociocultural DCLA revisited

This paper expands the sociocultural analysis of earlier discourse centric learning analytics (DCLA) to discuss the pedagogic functions of discourse, and the implications of these functions for DCLA. Given the importance of discourse for learning [13], and the potential of computers to (a) scaffold effective discourse and (b) give meaningful feedback on such discourse, it is important that DCLA are well theorised. Sociocultural theory emphasises context, and discourse “in action” in its analysis. If DCLA wishes to ground itself in such theory, work will need to be done to address these aspects of discourse in computational analysis. Given the potential of DCLA to provide support for educational talk – an important aspect of learning – research should be conducted to further develop DCLA approaches to such talk