A s a field, computer science faces a problem. From 2000 to 2004, the percentage of first-year undergraduates planning to major in CS declined by more than 60 percent (see the "Declining Interest in Computer Science" sidebar). 1 To attract more students, the introductory CS curriculum must be motivating and relevant. CS courses that are set in a motivating context (for example, using multimedia, gaming, or robotics) can excite students and get them hooked. Other researchers have worked on introductory programming classes with robots as well as introduction to robotics classes (http://myro. roboteducation.org/robobiblio). We didn't want to create a robotics course but rather an introductory CS course based on robots. Introduced properly, robots make visible and tangible those aspects of CS that are often hidden behind computer screens and in computer memory. To further this goal, we formed the Institute for Personal Robots in Education (IPRE), a joint effort between Georgia Tech and Bryn Mawr College and sponsored by Microsoft Research (www.roboteducation. org). This article discusses the first-year results of a three-year project.

Studies have shown altered task-based brain functioning as a result of cannabis use. To date, however, whether similar alterations in baseline resting state and functional organization of neural activity are observable in cannabis users remains unknown. We characterized global resting state cortical activations and functional connectivity via electroencephalography (EEG) in cannabis users and related these activations to measures of cannabis use. Resting state EEG in the eyes closed condition was collected from age- and sex-matched cannabis users (N = 17; 6 females; mean age = 30.9 ± 7.4 years) and non-using controls (N = 21; 9 females; mean age = 33.1 ± 11.6 years). Power spectral density and spectral coherence were computed to determine differences in cortical activations and connectivity between the two groups in the delta (1-4Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (31-50 Hz) frequency bands. Cannabis users exhibited decreased delta and increased theta, beta, and gamma power compared to controls, suggesting increased cortical activation in resting state and a disinhibition of inhibitory functions that may interrupt cognitive processes. Cannabis users also exhibited increased interhemispheric and intrahemispheric coherence relative to controls, reduced mean network degree, and increased clustering coefficient in specific regions and frequencies. This increased cortical activity may indicate a loss of neural refinement and efficiency that may indicate a "noisy" brain. Lastly, measures related to cannabis use were correlated with spectral power and functional connectivity measures, indicating that specific electrophysiological signals are associated with cannabis use. These results suggest that there are differences in cortical activity and connectivity between cannabis users and non-using controls in the resting state that may be related to putative cognitive impairments and can inform effectiveness of intervention programs.

Concomitant cannabis and nicotine use is more prevalent than cannabis use alone; however, to date, most of the literature has focused on associations of isolated cannabis and nicotine use limiting the generalizability of existing research. To determine differential associations of concomitant use of cannabis and nicotine, isolated cannabis use and isolated nicotine use on brain network connectivity, we examined systems-level neural functioning via independent components analysis (ICA) on resting state networks (RSNs) in cannabis users (CAN, n = 53), nicotine users (NIC, n = 28), concomitant nicotine and cannabis users (NIC + CAN, n = 26), and non-users (CTRL, n = 30). Our results indicated that the CTRL group and NIC + CAN users had the greatest functional connectivity relative to CAN users and NIC users in 12 RSNs: anterior default mode network (DMN), posterior DMN, left frontal parietal network, lingual gyrus, salience network, right frontal parietal network, higher visual network, insular cortex, cuneus/precuneus, posterior cingulate gyrus/middle temporal gyrus, dorsal attention network, and basal ganglia network. Post hoc tests showed no significant differences between (1) CTRL and NIC + CAN and (2) NIC and CAN users. These findings of differential associations of isolated vs. combined nicotine and cannabis use demonstrate an interaction between cannabis and nicotine use on RSNs. These unique and combined mechanisms through which cannabis and nicotine influence cortical network functional connectivity are important to consider when evaluating the neurobiological pathways associated with cannabis and nicotine use.

Cannabis use affects cortico-striatal networks that are essential for producing movement. In this review, we summarize the literature on motor system dysfunction in cannabis users and provide a rationale for why motor learning should be considered an important area in cannabis research. A majority of studies have addressed cognitive impairments in cannabis users and some have focused on driving performance, motor impulsivity, and motor inhibition. Our review of the literature has found that cannabis use is associated with motor performance impairments; however, there is a gap in the literature regarding impairments in motor learning. The involvement of the cortico-striatal network in both cannabis addiction and movement also suggests potential avenues for treatment and rehabilitation via the motor system.

It is well acknowledged that motor sequences can be learned quickly through online learning. Subsequently, the initial acquisition of a motor sequence is boosted or consolidated by offline learning. However, little is known whether offline learning can drive the fast learning of motor sequences (i.e., initial sequence learning in the first training session). To examine offline learning in the fast learning stage, we asked four groups of young adults to perform the serial reaction time (SRT) task with either a fixed or probabilistic sequence and with or without preliminary knowledge (PK) of the presence of a sequence. The sequence and PK were manipulated to emphasize either procedural (probabilistic sequence; no preliminary knowledge (NPK)) or declarative (fixed sequence; with PK) memory that were found to either facilitate or inhibit offline learning. In the SRT task, there were six learning blocks with a 2 min break between each consecutive block. Throughout the session, stimuli followed the same fixed or probabilistic pattern except in Block 5, in which stimuli appeared in a random order. We found that PK facilitated the learning of a fixed sequence, but not a probabilistic sequence. In addition to overall learning measured by the mean reaction time (RT), we examined the progressive changes in RT within and between blocks (i.e., online and offline learning, respectively). It was found that the two groups who performed the fixed sequence, regardless of PK, showed greater online learning than the other two groups who performed the probabilistic sequence. The groups who performed the probabilistic sequence, regardless of PK, did not display online learning, as indicated by a decline in performance within the learning blocks. However, they did demonstrate remarkably greater offline improvement in RT, which suggests that they are learning the probabilistic sequence offline. These results suggest that in the SRT task, the fast acquisition of a motor sequence is driven by concurrent online and offline learning. In addition, as the acquisition of a probabilistic sequence requires greater procedural memory compared to the acquisition of a fixed sequence, our results suggest that offline learning is more likely to take place in a procedural sequence learning task.