The document discusses research into analyzing different content channels, such as digital ink, speech, and slides, from classroom lectures recorded using a tablet PC. The researchers explored handwriting recognition, the relationship between written and spoken words, identifying attentional marks on slides and their associated content, and recognizing correction activities. The results showed basic handwriting recognition was surprisingly accurate, a strong co-occurrence between written and spoken words, the ability to identify attentional marks and linked content, and potential to recognize some high-level activities like corrections. The research aimed to better understand real presentation data to guide building tools for automatic analysis of educational content channels.

1. Speech, Ink, and Slides: The Interaction of Content Channels Richard Anderson Crystal Hoyer Craig Prince Jonathan Su Fred Videon Steve Wolfman Repeat Intro of Self Mention: -Richard -Jonathan In Audience

4. Distance Learning Classes

12. Handwriting Recog. Results Mention That These Results Are Surprisingly Good! Each Row Represents a Different Lecturer 260 (21%) 18 (1%) 123 (10%) 850 (68%) Total 58 (11%) 2 <(1%) 46 (9%) 408 (79%) Prof. E 111 (26%) 9 (2%) 45 (11%) 262 (61%) Prof. D 19 (44%) 1 (3%) 5 (11%) 18 (42%) Prof. C 71 (29%) 6 (2%) 26 (10%) 146 (59%) Prof. B 1 (6%) 0 (0%) 1 (6%) 16 (88%) Prof. A None Close Alternate Exact

17. Speech/Text Co-occurrence Results Each Row Represents a Different Lecturer

20. Example

25. Attentional Ink Recognition Accuracy 532 118 (22%) 50 (9%) 35 (7%) 329 (62%) 87 35 (40%) 0 (0%) 0 (0%) 52 (60%) Bullets 339 66 (20%) 44 (13%) 22 (6%) 207 (61%) Underlines 106 17 (16%) 6 (6%) 13 (12%) 70 (66%) Circles Non-Match Close Exact to Punctuation Exact

29. Example Results No Table Because: 1. Not a robust experiment 2. Proof of Concept