Model konsep integriti ke arah peningkatan kualiti penilaian rakan (peer assessment)

mereka dalam sesuatu tugasan. Namun, penilaian rakan ini kurang diamalkan di institusi pengajian tinggi kerana kualiti penilaian ini masih diragui terutamanya dari aspek integriti pelajar sebagai penilai. Sehubungan itu, kajian ini mencadangkan satu model integriti ke arah peningkatan kualiti penilaian rakan. Kajian ini dijalankan menggunakan reka bentuk multiphase yang terdiri daripada tiga (3) fasa. Pada fasa I (Analisis dokumen dan temu bual), dokumen daripada tahun 2010 hingga 2018 telah digunakan dan temu bual daripada enam (6) pakar dalam bidang Pendidikan Teknikal dan Vokasional (PTV) telah memperoleh tiga (3) elemen integriti iaitu, Integriti Diri (Motivasi diri, keberanian, disiplin diri, dan ketelusan), Interaksi Sosial (Kejujuran, keadilan, konsisten, amanah, dan perpaduan), dan Komitmen Kerja (Usaha, tanggungjawab, dan etika). Bagi fasa II (Pembangunan instrumen) pula, teknik Modified Delphi (MD) digunakan bagi memperoleh konsensus daripada pakar mengenai item-item yang dibina. Hasil daripada persetujuan pakar MD terdapat 90 item digunakan dalam kajian rintis I dan dianalisis menggunakan Winsteps; dan 19 item telah disingkirkan. Seterusnya, Kajian rintis II dijalankan bagi tujuan mendapatkan kesahan dan kebolehpercayaan item yang digunakan menggunakan Exploratory Factor Analysis (EFA). Hasil analisis EFA mendapati 3 item bertindih dan disingkirkan bagi menjalankan fasa akhir. Pada Fasa III (Pembangunan model), sebanyak 543 soal selidik telah dianalisis dengan menggunakan Structural Equation Modelling (SEM-AMOS). Hasil Miximum Likelihood Estimates menunjukkan nilai C.R > ± 1.96 bagi pekali regresi antara integriti dan kualiti penilaian adalah positif dan signifikan (β = 0.85, C.R = 12.558, p < 0.001). Ini menggambarkan bahawa integriti mempengaruhi kualiti penilaian. Manakala, kesediaan memperuntukkan masa merupakan partial mediator (rc= 0.23) yang sederhana penting kepada integriti dan kualiti penilaian rakan secara tidak langsung. Namun, jantina merupakan full moderator kepada kesan integriti terhadap kualiti penilaian rakan kerana pelajar perempuan mendapatkan nilai Δχ2 = 16.754 lebih besar daripada 3.84 berbanding dengan lelaki iaitu nilai Δχ2 = 3.218. Model integriti boleh dijadikan sebagai satu model panduan yang digunakan oleh pensyarah bagi mengaplikasikan penilaian rakan secara sistematik dan meramalkan kemungkinan yang akan berlaku semasa aktiviti penilaian ini dijalankan

On intelligible multimodal visual analysis

Analyzing data becomes an important skill in a more and more digital world. Yet, many users are facing knowledge barriers preventing them to independently conduct their data analysis. To tear down some of these barriers, multimodal interaction for visual analysis has been proposed. Multimodal interaction through speech and touch enables not only experts, but also novice users to effortlessly interact with such kind of technology. However, current approaches do not take the user differences into account. In fact, whether visual analysis is intelligible ultimately depends on the user. In order to close this research gap, this dissertation explores how multimodal visual analysis can be personalized. To do so, it takes a holistic view. First, an intelligible task space of visual analysis tasks is defined by considering personalization potentials. This task space provides an initial basis for understanding how effective personalization in visual analysis can be approached. Second, empirical analyses on speech commands in visual analysis as well as used visualizations from scientific publications further reveal patterns and structures. These behavior-indicated findings help to better understand expectations towards multimodal visual analysis. Third, a technical prototype is designed considering the previous findings. Enriching the visual analysis by a persistent dialogue and a transparency of the underlying computations, conducted user studies show not only advantages, but address the relevance of considering the user’s characteristics. Finally, both communications channels – visualizations and dialogue – are personalized. Leveraging linguistic theory and reinforcement learning, the results highlight a positive effect of adjusting to the user. Especially when the user’s knowledge is exceeded, personalizations helps to improve the user experience. Overall, this dissertations confirms not only the importance of considering the user’s characteristics in multimodal visual analysis, but also provides insights on how an intelligible analysis can be achieved. By understanding the use of input modalities, a system can focus only on the user’s needs. By understanding preferences on the output modalities, the system can better adapt to the user. Combining both directions imporves user experience and contributes towards an intelligible multimodal visual analysis