Who Signs Up and Who Stays? Attraction and Retention in an After-School Computer-Supported Program

We report findings from a study assessing computer-supported curriculum designed to engage low SES, underrepresented minority middle school students enrolled in an afterschool program with collaborative tasks that build 21st century skills, particularly related to digital literacy. Early in the program, we collected survey data from participants and from a sample of after-school attendees who decided not to enroll in our program concerning their goals, feelings toward STEM, and experiences with and access to technology. Over the first 7 weeks of programming, we also have collected attendance records. We report findings relating students’ individual factors at program onset to their attraction to and retention in our program. Our findings shed light on important issues relevant to the CSCL community and the conference theme, including identifying potential for attrition among students and engaging a diverse pool of students in computer-supported collaborative learning

A framework to analyze argumentative knowledge construction in computer-supported collaborative learning

Computer-supported collaborative learning (CSCL) is often based on written argumentative discourse of learners, who discuss their perspectives on a problem with the goal to acquire knowledge. Lately, CSCL research focuses on the facilitation of specific processes of argumentative knowledge construction, e.g., with computer-supported collaboration scripts. In order to refine process-oriented instructional support, such as scripts, we need to measure the influence of scripts on specific processes of argumentative knowledge construction. In this article, we propose a multi-dimensional approach to analyze argumentative knowledge construction in CSCL from sampling and segmentation of the discourse corpora to the analysis of four process dimensions (participation, epistemic, argumentative, social mode)

Towards Predictable Process and Consequence Attributes of Data-Driven Group Work: Primary Analysis for Assisting Teachers with Automatic Group Formation

Data-driven platforms with rich data and learning analytics applications provide immense opportunities to support collaborative learning such as algorithmic group formation systems based on learning logs. However, teachers can still get overwhelmed since they have to manually set the parameters to create groups and it takes time to understand the meaning of each indicator. Therefore, it is imperative to explore predictive indicators for algorithmic group formation to release teachers from the dilemma with explainable group formation indicators and recommended settings based on group work purposes. Employing learning logs of group work from a reading-based university course, this study examines how learner indicators from different dimensions before the group work connect to the subsequent group work processes and consequences attributes through correlation analysis. Results find that the reading engagement and previous peer ratings can reveal individual achievement of the group work, and a homogeneous grouping strategy based on reading annotations and previous group work experience can predict desirable group performance for this learning context. In addition, it also proposes the potential of automatic group formation with recommended parameter settings that leverage the results of predictive indicators

A case study of collaborative learning among preparatory year students and their teachers at Hail University in Saudi Arabia

The concept of collaborative learning (CL) relates to the educational use of small groups, in which students work together to maximise their learning and to teach and learn from each other as much as possible, after receiving guidelines and instructions from their teachers. Collaborative learning in Saudi higher education (SHE) has been promoted at the government level in recent years as part of a trend to increase the adoption of e-learning. The policy also aligns with educational reforms and the drive to make the Saudi economy more competitive and diverse. Nevertheless, it is still enforcing itself to become a norm in the teaching and learning process as it is a radical shift from the traditional centralised decision making in educational settings and teacher-centred teaching, which indicate a high power distance structure. Therefore, this study investigates the perceptions of preparatory year students and teachers at Hail University regarding the implementation of CL. A qualitative research methodology was adopted. Data were gathered from observations, six focus groups (composed of five students in each group) and individual interviews with 12 teachers on the foundation year. The findings of this study indicated two modalities for deploying CL: traditional CL (TCL/non-computer- supported collaborative learning [CSCL]) and computer-supported CL (CSCL) in Saudi higher Education. Furthermore, the results showed that CL indeed provides personal, social, and academic benefits. It is still, however, marred by challenges such that effective implementation is curtailed and thus does not produce positive learning outcomes among students. Overall, given the cultural background, the preference for retaining a high power distance, and what teachers and students are accustomed to, the study suggests further research be conducted to implement an form of CL adapted to suit Saudi culture