Global ETD Search

321	Managing multi-grade teaching for optimal learning in Gauteng West primary schools Tredoux, Marlise 01 1900 (has links) The researcher investigated the management of multi-grade teaching for optimal learning in Gauteng West primary schools. Ten participants, including school principals, heads of departments and educators participated in individual and focus group interviews and in observation of multi-grade classroom contexts. Findings revealed that educators involved in multi-grade teaching feel overwhelmed by challenging work conditions pertaining to large learner numbers and a lack of adequate didactical resources. This is exacerbated by a lack of professional development by means of tailor-made training for multi-grade teaching and the presumption that educators teaching such classes must merely change the monograde teaching format of the curriculum themselves for applicable implementation in a multi-grade teaching context. This leaves educators socially, emotionally and professionally isolated. Recommendations include the involvement of seasoned educators with expert knowledge and experience of multi-grade teaching to present training sessions constituting advice and support to inexperienced educators involved in said teaching. / Die navorser het die bestuur van meergraadonderrig by laerskole in Wes-Gauteng vir optimale leer ondersoek. Afgesien van individuele en fokusgroeponderhoude met skoolhoofde, departementshoofde en opvoeders, is waarneming in meergraadklaskamers gedoen. Volgens die bevindings bemoeilik groot klasse en ŉ gebrek aan didaktiese hulpmiddels meergraadopvoeders se taak. Meergraadopvoeders voel hulle geensins opgewasse teen hierdie werksomstandighede nie. ŉ Gebrek aan opleiding in meergraadonderrig en die veronderstelling dat opvoeders die eengraadformaat van die kurrikulum in ŉ meergraadformaat kan omskakel, vererger sake. Opvoeders is van mening dat hulle maatskaplik, emosioneel en professioneel in die steek gelaat word. Daar word aanbeveel dat gesoute opvoeders met kennis van en ervaring in meergraadonderrig onervare opvoeders oplei en adviseer. / Monyakisisi o dirile dinyakisiso ka ga go ruta dikereiti tse fapanego go fihlelela bokgoni le tsebo tikologong ya go thekga dinyakwa tsa baithuti dikolong tsa phoraemari go la Gauteng Bodikela. Batseakarolo ba lesome, go akaretswa dihlogo tsa dikolo, dihlogo tsa dikgoro le barutisi ba tseere karolo ditherisanong ka botee le dihlopha tseo di nepisitswego gape le temogo dikemong tsa diphaposi tsa dikereiti tse di fapanego. Dikhwetso di utollotse gore barutisi bao ba rutago dikereiti tse fapanego ba imelwa ke maemo a modiro wo o nyakago gore ba ntshe bokgoni bja bona ka moka ka lebaka la dipalo tse ntsi tsa baithuti le tlhokego ya dithusi tsa thuto tse di lekanego. Se se thatafiswa ke tlhokego ya tlhabollo ya profesene ye ka go fa tlhahlo yeo e lebanego ya go ruta dikreiti tse fapanego le kgopolo ya go re barutisi bao ba rutago ba swanela go no fetola popego ya lenaneothuto la kereiti e tee ka bobona go re ba le dirise kemong ya go ruta dikereiti tse fapanego. Se se dira gore barutisi ba ikhwetse ba se na kgokagano le setshaba leagong, ba hloka bao ba ka llelago go bona le go se be le bao ba nago le kgahlego go profesene ya bona. Ditshisinyo di akaretsa go ba gona ga barutisi bao e lego kgale ba ruta ba nago le maitemogelo le botsebi go ruta dikereiti tse fapanego go hlagisa dipaka tsa tlhahlo tseo di fago maele le thuso go barutisi bao ba se nago maitemogelo. / Educational Management and Leadership / M. Ed. (Education Management) Multi-grade teaching Mono-grade teaching environment HoDs (heads of department) Overcrowded classrooms Assistant teacher Cooperative group work Experienced educator Professional development Training for multi-grade teaching Educator-learner ratio School principal Meergraadonderrig Enkelgraadonderrigomgewing DH’s (departementshoofde) Bestuurstrategieë vir meergraadonderrig Oorvol klaskamers Assistentonderwyser Samewerkende groepwerk Ervare opvoeder Professionele ontwikkeling Opleiding in meergraadonderrig Opvoeder-leerderverhouding Skoolhoof Go ruta dikreiti tse fapanego Tikologo ya go ruta kereiti e tee diHoD (dihlogo tsa dikgoro) Morutisi yo a thusago Morutisi yo a nago le maitemogelo Tlhabollo ya profesene Tekanyo ya morutisi le baithuti Hlogo ya sekolo 372.125
322	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik

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Managing multi-grade teaching for optimal learning in Gauteng West primary schools

Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms