Filtering by
- All Subjects: human-computer interaction
- Creators: Gaffar, Ashraf
The purpose of this project was to evaluate the State Bar of New Mexico's (SBNM) new podcast series, SBNM is Hear. The podcast was initially developed as a member outreach tool and a new platform for professional development and survey questions were developed to gauge the podcast’s effectiveness in these two areas. An electronic survey was deployed to active members of the SBNM through email. Respondents were asked questions regarding their demographics, whether they had listened to the series, and what content they would like to hear in the future. The survey resulted in 103 responses, of which 60% indicated that they had not listened to the podcast. The results showed that listenership was evenly divided between generations and that more females listened to at least one episode. The open-ended responses indicated that the two cohorts of respondents (listeners and non- listeners) viewed the podcast a potential connection to the New Mexico judiciary. Future recommendations include conducting an annual survey to continue to understand the effectiveness of the podcast and solicit feedback for continued growth and improvement
skills. In children with autism, the development of these skills can be delayed. Applied
behavioral analysis (ABA) techniques have been created to aid in skill acquisition.
Among these, pivotal response treatment (PRT) has been empirically shown to foster
improvements. Research into PRT implementation has also shown that parents can be
trained to be effective interventionists for their children. The current difficulty in PRT
training is how to disseminate training to parents who need it, and how to support and
motivate practitioners after training.
Evaluation of the parents’ fidelity to implementation is often undertaken using video
probes that depict the dyadic interaction occurring between the parent and the child during
PRT sessions. These videos are time consuming for clinicians to process, and often result
in only minimal feedback for the parents. Current trends in technology could be utilized to
alleviate the manual cost of extracting data from the videos, affording greater
opportunities for providing clinician created feedback as well as automated assessments.
The naturalistic context of the video probes along with the dependence on ubiquitous
recording devices creates a difficult scenario for classification tasks. The domain of the
PRT video probes can be expected to have high levels of both aleatory and epistemic
uncertainty. Addressing these challenges requires examination of the multimodal data
along with implementation and evaluation of classification algorithms. This is explored
through the use of a new dataset of PRT videos.
The relationship between the parent and the clinician is important. The clinician can
provide support and help build self-efficacy in addition to providing knowledge and
modeling of treatment procedures. Facilitating this relationship along with automated
feedback not only provides the opportunity to present expert feedback to the parent, but
also allows the clinician to aid in personalizing the classification models. By utilizing a
human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the
classification models by providing additional labeled samples. This will allow the system
to improve classification and provides a person-centered approach to extracting
multimodal data from PRT video probes.