Character reversal in children: the prominent role of writing direction

Recent research has established that 5- to 6-year-old typically developing children in a left–right writing culture spontaneously reverse left-oriented characters (e.g., they write Open image in new window instead of J) when they write single characters. Thus, children seem to implicitly apply a right-writing rule (RWR: see Fischer & Koch, 2016a). In Study 1, the reversal of all asymmetrical digits and capital letters by 356 children was modeled with a simple Rasch model, which describes reversal as the outcome of two competing responses, correct writing and writing in the cultural direction of writing. It accounts for the high frequency of reversals of the left-oriented characters (3, Z, J, 1, 2, 7, 9), as predicted by the RWR. Study 2 investigated letter reversals when children spontaneously write their name from right to left. Most of the 204 children in the study radically changed the direction of the RWR by reversing mainly the right-oriented letters (B, C, D, E, F, G, K, L, N, P, R, S). Hence, a more universal formulation of the RWR would be as an implicit rule orienting characters in the writing direction. This reformulated rule is consistent with the “spatial agency bias” model (Suitner & Maas, 2016), according to which writing direction affects thoughts and actions. Visual and motoric statistical learning may favor bootstrapping of the rule. Taken together, these data demonstrate the prominent role of culture in a phenomenon—character reversal or mirror writing—which has often been presented uniquely as biologically determined.     

The Effects of Simultaneous Prompting on Teaching Expressive Identification of Objects: An instructive feedback study

The present study examined whether the use of a simultaneous prompting procedure would result in an increase in the percentage of correct responses when expressively identifying first aid materials. A multiple probe design across behaviours and replicated across students was used. Three training sets with a total of nine first aid materials were presented to three students. Also, instructive feedback stimuli were presented during consequent events to increase the efficiency of instruction. The instructive feedback con tained the functions of the instructional materials. Maintenance effects were assessed 1, 2, and 4 weeks after training. The results showed that all students learned expressive identification of first aid materials and maintained them after training. Furthermore, all students acquired and maintained some of the instructive feedback stimuli presented to them during instruction.     

Formative Evaluation of Computer‐Based Training For a University Financial System

This article describes a formative evaluation of a one‐day introductory computer‐based training (CBT) course for a new on‐line financial and purchasing system at a large public university. The purposes of the evaluation were to evaluate the effectiveness of the training and to identify appropriate revisions and incorporate them into the training program. Participants were 78 university employees who were likely future users of the financial and purchasing system. The mean score on an on‐line performance posttest that simulated real‐work tasks was 94%, and the mean on a 40‐item knowledge posttest covering the CBT content was 74%. Learner attitudes toward the course were positive, averaging 4.4 on a 5‐point scale. Formative evaluation of the program resulted in revisions that had the potential to improve its effectiveness and provided evidence of the value of ongoing formative evaluation of workplace training.     

Using natural language processing to support peer-feedback in the age of artificial intelligence: A cross-disciplinary framework and a research agenda

Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments.

Practitioner notes

What is already known about this topic

• There is considerable research in educational science on peer-feedback processes.
• Natural language processing facilitates the analysis of students' textual data.
• There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer-feedback process.

What this paper adds

• A comprehensive overview model that describes the relevant activities and products in the peer-feedback process.
• A terminological and procedural scheme for designing NLP-based adaptive support measures.
• An application of this scheme to the peer-feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer-feedback.

Implications for practice and/or policy

• To boost the effectiveness of their peer-feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings.
• Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions.