ReaderBench, an Environment for Analyzing Text Complexity and Reading Strategies

Session: Educational Data MiningInternational audienceReaderBench is a multi-purpose, multi-lingual and flexible environment that enables the assessment of a wide range of learners' productions and their manipulation by the teacher. ReaderBench allows the assessment of three main textual features: cohesion-based assessment, reading strategies identification and textual complexity evaluation, which have been subject to empirical validations. ReaderBench covers a complete cycle, from the initial complexity assessment of reading materials, the assignment of texts to learners, the capture of metacognitions reflected in one's textual verbalizations and comprehension evaluation, therefore fostering learner's self-regulation process

Online Knowledge Communities as Student-Centered Open Learning Environments: How Likely Will They Be to Integrate Learners as New Members?

Using online knowledge communities (OKCs) from the Internet as studentcentered, open learning environments (SCOLEs) poses the question how likely these communities will be to integrate learners as new members. This premise of learning in SCOLEs is analyzed in the current study. Based on the approaches of voices interanimation and polyphony, a natural language processing tool was employed for dialog analysis in integrative vs. non-integrative blog-based OKCs. Three dialog dimensions were identified: participants’ individual content-oriented contribution, social contribution, and their position within the social network. Hierarchical clusters built upon these dimensions reflect sociocognitive structures including central, regular and peripheral OKC members. OKCs with a stronger layer of regular members appear more likely to integrate new members, whereas OKCs with a stronger layer of peripheral members appear less likely to do so. Consequently, the study suggests an automated prediction method of OKC integrativity that may sustain the educational use of OKCs

Finding Student-Centered Open Learning Environments on the Internet: Automated Dialogue Assessment in Academic Virtual Communities of Practice

Starting from the socio-constructivist concepts of (virtual) community of practice (vCoP) and internet-based argumentative open-ended learning environments, this study proposes and validates two tools for automated dialogue assessment, ReaderBench and Important Moments, developed on the ground of the polyphonic social knowledge building model. The analyzed corpus was the dialogue produced by an academic vCoP with N = 179 community members in 23 months, and consisting of 3685 interventions in 292 text-based discussion threads. The analysis results uncovered significant differences in the discussion threads produced by central and peripheral participants, such that central participants produced more interventions with higher collaborative dialogue quality, and the discussion threads they initiated were longer and involved a larger number of participants. Moreover, based on the automated analysis result, the vCoP participants could be classified in two clusters corresponding to the well-known core-periphery structure of CoPs. These findings are consistent with those revealed by other methods, and suggest that the employed tools are appropriate for identifying virtual communities that are appropriate as open-ended learning environments. Further research and development is needed to deepen quantitative vCoP models and test communication strategies recommended to students in vCoP-based argumentative open-ended learning environments